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ORGANIZATIONAL BARRIERS TO TECHNOLOGY
ADOPTION: EVIDENCE FROM SOCCER-BALL PRODUCERS
IN PAKISTAN∗
DAVID ATKIN
AZAM CHAUDHRY
SHAMYLA CHAUDRY
AMIT K. KHANDELWAL
ERIC VERHOOGEN
This article studies technology adoption in a cluster of soccer-ball producers
in Sialkot, Pakistan. We invented a new cutting technology that reduces waste of
the primary raw material and gave the technology to a random subset of producers. Despite the clear net benefits for nearly all firms, after 15 months take-up
remained puzzlingly low. We hypothesize that an important reason for the lack of
adoption is a misalignment of incentives within firms: the key employees (cutters
and printers) are typically paid piece rates, with no incentive to reduce waste, and
the new technology slows them down, at least initially. Fearing reductions in their
effective wage, employees resist adoption in various ways, including by misinforming owners about the value of the technology. To investigate this hypothesis, we
implemented a second experiment among the firms that originally received the
technology: we offered one cutter and one printer per firm a lump-sum payment,
∗ We
are grateful to Tariq Raza, our project manager, and to Research Consultants (RCONS), our local survey firm, in particular Irfan Ahmad, Kashif Abid and
Syed Hassan Raza Rizvi, for tireless work in carrying out the firm surveys; to Fatima Aqeel, Muhammad Haseeb Ashraf, Sabyasachi Das, Golvine de Rochambeau,
Abdul Rehman Khan, Menghan Xu, and Daniel Rappoport for excellent research
assistance; to the editor (Pol Antrás) and four anonymous referees whose suggestions greatly improved the manuscript; to Laura Alfaro, Charles Angelucci, Simon
Board, Esther Duflo, Florian Ederer, Cecilia Fieler, Dean Karlan, Navin Kartik,
Asim Khwaja, Rocco Macchiavello, David McKenzie, Moritz Meyer-ter-Vehn, Ben
Olken, Ralph Ossa, Simon Quinn, Anja Sautmann, Chris Udry, Johannes Van
Biesebroeck, Rodrigo Wagner, Reed Walker, Chris Woodruff, Daniel Xu, and many
seminar audiences for helpful discussions; and to the International Growth Centre,
especially Ijaz Nabi and Naved Hamid, and the Private Enterprise Development
in Low-Income Countries research initiative for generous research support. We are
particularly indebted to Annalisa Guzzini, who shares credit for the invention of
the new technology described in the text, and to Naved Hamid, who first suggested
we study the soccer-ball sector in Sialkot. All errors are ours.
C The Author(s) 2017. Published by Oxford University Press on behalf of President
and Fellows of Harvard College. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License
(http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For
commercial re-use, please contact [email protected]
The Quarterly Journal of Economics (2017), 1101–1164. doi:10.1093/qje/qjx010.
Advance Access publication on March 9, 2017.
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approximately a month’s earnings, conditional on demonstrating competence in
using the technology in the presence of the owner. This incentive payment, small
from the point of view of the firm, had a significant positive effect on adoption.
The results suggest that misalignment of incentives within firms is an important
barrier to technology adoption in our setting. JEL Codes: O1, O3, D2, L2.
I. INTRODUCTION
Observers of the process of technological diffusion have been
struck by how slow it is for many technologies.1 A number of the
best-known studies have focused on agriculture or medicine,2 but
diffusion has also been observed to be slow among large firms in
manufacturing. In a classic study of major industrial technologies,
Mansfield (1961) found that it took more than 10 years for half
of major U.S. iron and steel firms to adopt by-product coke ovens
or continuous annealing lines, technologies that were eventually
adopted by all major firms. More recently, Bloom et al. (2013)
found that many Indian textile firms are not using standard (and
apparently cheap to implement) management practices that have
diffused widely elsewhere. The surveys by Hall and Khan (2003)
and Hall (2005) contain many more examples.
Why is adoption so slow for so many technologies? The question is key to understanding the process of economic development
and growth. It is also a difficult one to study empirically, especially among manufacturing firms. It is rare to be able to observe
firms’ technology use directly, and rarer still to have direct measures of the costs and benefits of adoption, or of what information
firms have about a given technology. As a consequence, it is difficult to distinguish between various possible explanations for low
adoption rates.
In this article, we present evidence from a cluster of soccerball producers in Sialkot, Pakistan, that a conflict of interest
between employees and owners within firms is an important
barrier to adoption. The cluster is economically significant,
1. In a well-cited review article, Geroski (2000, 604) writes: “The central feature of most discussions of technology diffusion is the apparently slow speed at
which firms adopt new technologies.” Perhaps the foremost economic historian of
technology, Nathan Rosenberg, writes “If one examines the history of the diffusion
of many inventions, one cannot help being struck by two characteristics of the
diffusion process: its apparent overall slowness on the one hand and the wide variations in the rates of acceptance of different inventions on the other” (Rosenberg
1972, 6).
2. See, for instance, Ryan and Gross (1943), Griliches (1957), Coleman, Katz,
and Menzel (1966), Foster and Rosenzweig (1995), and Conley and Udry (2010).
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1103
producing 30 million soccer balls a year, including for the largest
global brands (and the 2014 World Cup). The setting has two
main advantages for understanding the adoption process. The
first is that the industry is populated by a substantial number
of firms—135 by our initial count—producing a relatively standardized product, using largely the same, simple production process. The technology we focus on is applicable at a large enough
number of firms to conduct statistical inference. The second, and
perhaps more important, advantage is that our research team,
through a series of fortuitous events, discovered a useful innovation: we invented a new technology that represents, we argue, a
clear increase in technical efficiency for nearly all firms in the sector. The most common soccer-ball design combines 20 hexagonal
and 12 pentagonal panels. The panels are cut from rectangular
sheets of an artificial leather called rexine, typically by bringing
a hydraulic press down on a hand-held metal die. Our new technology, described in more detail below, is a die that increases the
number of pentagons that can be cut from a rectangular sheet by
implementing the densest packing of pentagons in a plane known
to mathematicians. A conservative estimate is that the new die
reduces rexine cost per pentagon by 6.76% and total costs by approximately 1%—a modest reduction but a nontrivial one in an
industry where mean profit margins are 8%. The new die requires
minimal adjustments to other aspects of the production process.
Importantly, we observe adoption of the new die very accurately,
in contrast to studies that must infer technology adoption from
changes in residual-based measures of productivity such as those
reviewed in Syverson (2011).
We randomly allocated the new technology to a subset of 35
firms (which we refer to as the “tech-drop” group) in May 2012. To
a second group of 18 firms (the “cash-drop” group) we gave cash
equal to the value of the new die (US$300), and to a third group of
79 firms (the “no-drop” group) we gave nothing. We expected the
technology to be adopted quickly by the tech-drop firms, and we
planned to focus on spillovers to the cash-drop and no-drop firms;
we are pursuing this line of inquiry in a companion project. In the
first 15 months of the experiment, however, the most striking fact
was how few firms adopted, even among the tech-drop group. As
of August 2013, five firms from the tech-drop group and one from
the no-drop group had used the new die to produce more than
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1,000 balls, our preferred measure of adoption.3 The experiences
of the adopters indicated that the technology was working as expected; we were reassured, for instance, by the fact that the one
no-drop adopter was one of the largest firms in the cluster and
had purchased a total of 40 dies on 15 separate occasions. Overall,
however, adoption remained puzzlingly low.
In our March–April 2013 survey round, we asked nonadopters
in the tech-drop group why they had not adopted. Of a large number of possible responses, the leading answer was resistance from
employees. Anecdotal evidence from a number of firms also suggested that workers were resisting the new die, in part by misinforming owners about its productivity benefits. Moreover, we
noticed that the pay scheme of the large adopter (purchaser of
the 40 dies) differed from the local norm: while more than 90% of
firms pay a pure piece rate, it pays a fixed monthly salary plus a
performance bonus.
The qualitative evidence led us to hypothesize that a misalignment of incentives within the firm is an important reason for
the lack of adoption. The new die slows cutters down, certainly in
the initial period when they are learning how to use it, and possibly in the longer run.4 If the piece rate is not adjusted, the cutters’
effective wage falls. Unless owners modify the payment scheme,
the benefits of using the new technology accrue to owners and the
costs are borne by the cutters. Realizing this, the workers resist
adoption, including by misinforming owners about the value of
the technology. We formalize this intuition in a simple model of
strategic communication between an imperfectly informed principal (the owner) and a perfectly informed agent (the cutter). When
principals are limited to standard piece-rate contracts that must
be set ex ante, there is an equilibrium in which the agent seeks to
discourage the principal from adopting a technology like ours and
influences the principal not to adopt.5 A simple modification to the
labor contract, conditioning the wage contract on marginal cost,
3. In this introduction, we use our “liberal” measure of adoption; see
Section III for a discussion of our “liberal” and “conservative” adoption measures.
4. Our data suggest that the long-run speed is only slightly slower than for
the existing die, although the workers did not know this prior to adoption and may
plausibly have been worried that they would be substantially slower in the long
run as well as the short.
5. If we assume, following Crawford and Sobel (1982), that players are able
to coordinate on the “most informative” equilibrium in every cheap-talk subgame,
then this equilibrium is unique.
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1105
an ex post–revealed characteristic of the technology, induces the
agent to encourage adoption of the technology and the principal
to adopt it.
To investigate the misalignment-of-incentives hypothesis, we
implemented a second experiment. In September 2013, we randomly divided the set of 31 tech-drop firms that were still in
business into two subgroups, a treatment subgroup (which we
call Group A) and a control subgroup (Group B). To Group B, we
simply gave a reminder about the benefits of the die and an offer
of another demonstration of the cutting pattern. To Group A, we
gave the reminder and explained the issue of misaligned incentives to the owner and offered an incentive payment treatment:
we offered to pay one cutter and one printer a lump-sum bonus
roughly equivalent to a month’s earnings (US$150 and US$120,
respectively), conditional on demonstrating competence with the
new technology in the presence of the owner. This bonus was designed to mimic (as closely as possible, given firms’ reluctance
to participate) the “conditional” wage contracts we model in the
theory. (We also paid printers a bonus because the new die requires a slight modification to the printing process, and printers
face a similar but weaker disincentive to adopt.) The one-time
bonus payments were small relative both to revenues from soccerball sales for the firms, approximately US$57,000 a month at the
median, and to the variable-cost reductions from adopting our
technology, approximately US$413 a month at the median.
Fifteen firms were assigned to Group A and 16 to Group B.
Of the 13 Group A firms that had not already adopted the new
die, 8 accepted the incentive payment intervention, and 5 adopted
the new die within six months of the second experiment. Of the
13 Group B firms that had not already adopted the new die, none
adopted within six months of the experiment and one adopted in
the next six-month period. Although the sample size is small, we
find a positive, statistically significant effect of the incentive intervention on adoption, with the probability of adoption increasing
by 0.27–0.32 from a baseline adoption rate of 0.16 in our intentto-treat specifications. Our results remain significant when using
permutation tests that are robust to small sample sizes. The fact
that such small payments had a significant effect on adoption suggests that the misalignment of incentives is indeed an important
barrier to adoption in this setting.
After the second experiment, we asked owners and their employees a number of survey questions about wage setting and communication within the firm, and the results are supportive of the
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hypotheses that piece-rate contracts are sticky and that cutters
have misinformed owners about the technology. We argue that the
results do not support a leading alternative hypothesis, that our
second experiment mechanically induced adoption by subsidizing
the fixed costs of adoption, because it cannot quantitatively
explain both low initial adoption rates and an increase in adoption
of the magnitude we find in response to the second experiment.
We also argue against the hypothesis that the second experiment
merely increased the salience of the technology to owners, and
that this alone led to increased adoption.
A natural question is why the firms themselves did not adjust
their payment schemes to incentivize their employees to adopt the
technology. Our model suggests two possible explanations. The
first is that owners simply did not realize that such an alternative
payment scheme was possible or desirable, just as the technical
innovation had not occurred to them. The second is that there was
a transaction cost involved in changing payment schemes that
exceeded the expected benefits in this case. In the end, the two
hypotheses have similar observable implications and are difficult
to distinguish empirically. The important point is that many firms
did not in fact adjust the payment scheme and as a consequence
there was scope for our modest payment intervention to have an
effect on adoption.
To our knowledge, this is the first study to randomly allocate a new technology to manufacturing firms to examine the
adoption process.6 But the article is related to several different
literatures. There is a long literature, largely descriptive, on how
conflicts of interest in the workplace may affect productivity. One
strand, dating back at least to the work of Taylor (1896, 1911)
on scientific management, and including classic studies by
Mathewson (1931), Roy (1952), Edwards (1979), and Clawson
(1980), has focused on how employees in piece-rate systems may
conceal information about how quickly they are capable of working or about productivity improvements they have discovered.7 A
distinctive aspect of our study, relative to this strand, is the focus on workers dissuading owners from using a technology from
6. A recent study by Hardy and McCasland (2016), which appeared after our
paper was widely circulated, randomly allocated a weaving technology to textile
producers in Ghana, but focuses on diffusion rather than barriers to adoption.
7. A more recent precursor is a case study by Freeman and Kleiner (2005),
which provides descriptive evidence that a U.S. shoe company’s shift away from
piece rates helped it increase productivity.
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1107
outside the firm. Another strand of literature, much of it focusing
on historical cases, has emphasized worker resistance to the adoption of such externally developed technologies (Lazonick 1979,
1990; Mokyr 1990). But the most commonly cited cases are of
resistance to labor-saving technologies by groups of skilled artisans whose skills were in danger of being rendered obsolete. By
contrast, our technology is labor-using: the new die simply slows
cutters down while they are learning to use it and possibly in the
longer run; we would expect demand for skilled cutters to increase
marginally. There appears to have been little previous empirical
research on worker resistance to such technologies.
A number of other explanations for slow technology adoption by manufacturing firms have been advanced. Bloom and
Van Reenen (2007, 2010) and Bloom et al. (2013) suggest that
a lack of product market competition may be responsible for
the failure to adopt beneficial management practices. Rosenberg
(1982), David (1990), Bresnahan and Trajtenberg (1995), and others have emphasized that new technologies often require changes
in complementary technologies, which take time to implement. In
our setting, firms sell almost all output on international export
markets that appear to be quite competitive, and our technology
requires extremely modest changes to other aspects of production, so these common explanations do not appear to be directly
applicable.
There is an active literature in development economics on
technology adoption in agriculture, where measures of technology use are more readily available than in manufacturing
(e.g., Foster and Rosenzweig 1995; Munshi 2004; Bandiera and
Rasul 2006; Conley and Udry 2010; Duflo, Kremer, and Robinson
2011; Suri 2011; Hanna, Mullainathan, and Schwartzstein 2014;
BenYishay and Mobarak 2014; Beaman et al. 2015; Emerick et al.
2016). In addition to being important for development in their own
right, manufacturing firms raise issues of organizational conflict
that do not arise when the decision makers are individual smallholder farmers. Also, risk arguably plays a less important role
among manufacturing firms, both because there is a lower degree of production risk (making inference about the benefits of a
technology more straightforward) and because factory owners are
typically richer and presumably less risk-averse than smallholder
farmers.8
8. Also related are recent papers on adoption of health technologies in the
presence of externalities (Miguel and Kremer 2004; Cohen and Dupas 2010; Dupas
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Our article is also related to a small but growing literature on
field experiments in firms, including the experiments with fruit
pickers by Bandiera, Barankay, and Rasul (2005, 2007, 2009) and
with microenterprises by de Mel, McKenzie, and Woodruff (2008).
The Bloom et al. (2013) study randomly allocated consulting services among Indian textile plants and argues that this generated
exogenous variation in management practices (arguably a form
of technology) which can be used to estimate the effect of those
practices on plant outcomes. The authors suggest that “informational constraints” are an important factor leading firms not to
adopt simple, apparently beneficial practices that are widespread
elsewhere. Our study investigates how conflicts of interest
within firms can impede the flow of information to managers
and provides a possible micro-foundation for such informational
constraints.9
The theoretical model we develop combines ideas from the
literature on strategic communication following Crawford and
Sobel (1982) and the voluminous literature on principal-agent
models of the employment relationship (reviewed, for instance, by
Lazear and Oyer 2013, and Gibbons and Roberts 2013). Gibbons
(1987) formalizes the argument that (assuming employers cannot
commit to future contracts) workers paid piece rates may hide
information about labor-saving productivity improvements from
their employers to prevent employers from reducing rates.10
Carmichael and MacLeod (2000) explore the contexts in which
firms will commit to fixing piece rates to alleviate these “ratchet”
effects. Holmstrom and Milgrom (1991) show that high-powered
incentives such as piece rates may induce employees to focus too
2014), on adoption of change-holding behavior by Kenyan retail micro-enterprises
(Beaman, Magruder, and Robinson 2014), and on adoption of energy efficient technologies by households and firms in the United States (Anderson and Newell 2004;
Fowlie, Greenstone, and Wolfram 2015a, 2015b). Again, organizational conflict
does not appear to play an important role in these settings.
9. Our study is also related to the “insider econometrics” literature reviewed by
Ichniowski and Shaw (2013), which focuses on relationships between management
practices and productivity, typically in a cross-sectional context, and generally does
not focus on technology adoption. A recent experimental study by Khan, Khwaja,
and Olken (2016) of tax collectors in Pakistan focuses on a related issue: the effect
of altering wage contracts on employee performance.
10. Lazear (1986) also notes that workers under a piece-rate scheme may
have an incentive to restrict output to avoid such a ratchet effect, but argues that
(assuming employers can commit to future contracts) this can be counteracted by a
suitable set of piece rates in all periods. The key difference between Lazear (1986)
and Gibbons (1987) is the assumption about the employer’s ability to commit.
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1109
much on the incentivized task to the detriment of other tasks,
which could include reporting accurately on the value of a technology. Our study supports the argument of Milgrom and Roberts
(1995) that piece rates may need to be combined with other incentives (in our case, higher pay conditional on adopting the new technology) to achieve high performance. Perhaps the closest theoretical antecedent is Dow and Perotti (2013), in which losers within
the firm may block the adoption of a new technology because it
reduces their share of quasi-rents (but do not communicate strategically).11 Garicano and Rayo (2016) review a number of models
of “organizational failures” that may lead firms not to adopt
surplus-enhancing innovations. We view our model primarily as
an application of insights from the organizational-economics literature to our setting, although we are not aware of a previous theoretical treatment of the specific idea that workers on piece-rate
contracts may dissuade owners from adopting surplus-enhancing
technologies from outside the firm. Our main contribution is
empirical: while the organizational-economics literature has had
to rely almost entirely on case studies for supporting evidence
on organizational failures, we are able to document—and explore
the reasons for—such a failure in an experimental setting.12
The article is organized as follows. Section II provides
background on the Sialkot cluster and describes the new cutting
technology. Section III describes our surveys and presents
summary statistics. Section IV details the roll-out of the new
technology and documents rates of early adoption. Section V
discusses qualitative evidence on organizational barriers and
summarizes our model of strategic communication in a principalagent context, the full exposition of which has been relegated to
Online Appendix B. Section VI describes the incentive payment
experiment and evaluates the results. Section VII presents
additional evidence on the hypothesized theoretical mechanisms.
Section VIII considers the leading alternative interpretations of
our findings, and Section IX concludes.
11. This idea has parallels in the work of Marglin (1974) and Stole and Zwiebel
(1996), who argue that owners may prefer technically less efficient technologies
that shift the distribution of rents in their favor. Also related, with very different
emphases, are Dearden, Ickes, and Samuelson (1990), Aghion and Tirole (1997),
Dessein (2002), and Krishna and Morgan (2008).
12. Whereas the literature reviewed by Garicano and Rayo (2016) has focused
on major technical disruptions, this article shows that firms have difficulty adapting even to incremental technological improvements. While the forgone surplus
is modest in this case, it seems plausible that such mini-failures are common in
organizations and that their cumulative effect is substantial.
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II. BACKGROUND AND DESCRIPTION OF THE NEW TECHNOLOGY
II.A. The Industry
Sialkot is a city of 1.6 million people in the province of Punjab,
Pakistan. The soccer-ball cluster dates to British colonial rule. In
1889, a British sergeant asked a Sialkoti saddlemaker to repair a
damaged ball, and then to make a new ball from scratch. The industry expanded through spinoffs and new entrants. (The history
of the industry is reviewed in more detail in Atkin et al. 2017.)
By the 1970s, the city had become a center of offshore production for many European soccer-ball companies. In 1982, a firm in
Sialkot manufactured the FIFA World Cup ball for the first time.
Virtually all of Pakistan’s soccer ball production is concentrated in
Sialkot and exported to foreign markets. While in recent years the
industry has faced increasing competition from East Asian (especially Chinese) suppliers,13 Sialkot remains the major source for
the world’s hand-stitched soccer balls. It provided, for example,
the hand-stitched balls used in the 2012 Olympic Games.14
To the best of our knowledge, there were 135 manufacturing firms producing soccer balls in Sialkot as of November 2011.
The firms employ approximately 12,000 workers, and outsourced
employment of stitchers in stitching centers and households is
estimated to be more than twice that number (Khan, Munir,
and Willmott 2007). The largest firms have hundreds of employees and produce for international brands such as Nike and
Adidas. These firms manufacture both high-quality “match” and
medium-quality “training” balls, often with a brand or team logo,
as well as lower quality “promotional” balls, often with an advertiser’s logo. The remaining producers are small- and medium-size
firms (the median firm size is 25 employees), which typically produce promotional balls either for clients they meet through industry fairs and online markets or under subcontract to larger
firms.
13. The evolution of soccer-ball imports to the United States (which tracks
soccer balls specifically, unlike other major importers) is shown in Online Appendix
Figure A.1.
14. The ball for the 2014 World Cup, also produced in Sialkot, used a new
thermo-molding technology.
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
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II.B. The Production Process
Before presenting our new technology, we briefly explain the
standard production process. As mentioned most soccer balls
(approximately 90% in our sample) are of a standard design
combining 20 hexagons and 12 pentagons (see Online Appendix
Figure A.2). There are four stages of production. In the first stage,
shown in Online Appendix Figure A.3, layers of cloth (cotton
and/or polyester) are glued to an artificial leather called rexine
using a latex-based adhesive, to form what is called a laminated
rexine sheet (henceforth “laminated rexine”). The rexine, cloth,
and latex are the most expensive inputs to production, together
accounting for approximately 46% of the total cost of each soccer
ball (or more if higher-quality rexine, which tends to be imported,
is used). In the second stage, shown in Online Appendix Figure
A.4, a skilled cutter uses a metal die and a hydraulic press to
cut the hexagonal and pentagonal panels from the laminated rexine. The cutter positions the die by hand before activating the
press with a foot pedal. He then slides the rexine along and places
the die again to make the next cut.15 In the third stage, shown in
Online Appendix Figure A.5, logos or other insignia are printed on
the panels. This requires a “screen,” held in a wooden frame, that
allows ink to pass through to create the desired design. Typically
the dies cut pairs of hexagons or pentagons, making an indentation between them but leaving them attached to be printed as a
pair, using one swipe of ink. In the fourth stage, shown in Online
Appendix Figure A.6, the panels are stitched together around an
inflatable bladder. Unlike the previous three stages, this stage
is often outsourced to specialized stitching centers or stitchers’
homes. This stage is the most labor-intensive part of the production process, accounting for approximately 71% of total labor costs.
The production process is remarkably similar across the
range of firms in Sialkot. A few larger firms have automated the
cutting process, cutting half or full laminated rexine sheets at
once, or attaching a die to a press that moves on its own, but even
these firms typically continue to do hand cutting for a substantial
share of their production. A few firms in the cluster have implemented machine stitching, but this has no implications for the
first three stages of production.
Prior to our study, the most commonly used dies cut two panels at a time with the panels sharing an entire edge (Figure I).
15. All of the owners and employees we have encountered have been men.
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FIGURE I
Traditional Two-Hexagon and Two-Pentagon Dies
Hexagons tessellate (i.e., completely cover a plane), and experienced cutters are able to cut with a small amount of waste—
approximately 8% of a laminated rexine sheet, mostly around
the edges. (See the laminated rexine “net” remaining after cutting hexagons in Figure II.) By contrast, pentagons do not tessellate, and using the traditional two-pentagon die even experienced
cutters typically waste 20–24% of the laminated rexine sheet
(Figure III). The leftover laminated rexine has little value; typically it is sold to brickmakers who burn it to fire their kilns.
II.C. The Innovation
In June 2011, as we were first exploring the possibility of
studying the soccer-ball sector, we sought out a consultant who
could recommend a beneficial new technique or practice that had
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1113
FIGURE II
Laminated Rexine Wastage, Hexagons
Figure displays laminated rexine wastage from cutting hexagons with the traditional two-hexagon die.
not yet diffused in the industry. We found a Pakistan-based consultant who appears to have been responsible for introducing the existing two-hexagon and two-pentagon dies many years ago. (Previously, firms had used single-panel dies.) We offered the consultant
US$4,125 to develop a cost-saving innovation for us. The consultant spent several days in Sialkot but was unable to improve
on the existing technology. After this setback, Eric Verhoogen (a
coauthor on this project) happened to watch a YouTube video of
a Chinese firm producing the Adidas “Jabulani” thermo-molded
soccer ball used in the 2010 FIFA World Cup. The video showed
an automated press cutting pentagons for an interior lining of
the Jabulani ball, using a pattern different from the one we knew
was being used in Sialkot (Online Appendix Figure A.7). Based
on the pattern in the video, Verhoogen and Annalisa Guzzini,
an architect, developed a blueprint for a four-pentagon die
(Figure IV).16 Through an intermediary, we contracted with a
16. One might wonder whether firms in Sialkot also observed the production
process in the Chinese firm producing for Adidas, since it was so easy for us to
do so. We found one owner, of one of the larger firms in Sialkot, who said that he
had been to China and observed the offset cutting pattern (illustrated in the left
panel in Figure IV) and was planning to implement it on a new large cutting press
to cut half a laminated rexine sheet at once, a process known as “table cutting.”
As of May 2012, he had not yet implemented the new pattern, however, and he
had not developed a hand-held offset die. It is also important to note that two of
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FIGURE III
Laminated Rexine Wastage, Pentagons, Traditional Die
Figure displays laminated rexine wastage from cutting pentagons with the traditional two-pentagon die.
diemaker in Sialkot to produce the die (Online Appendix Figure
A.8). It was only after we had received the first die and piloted
it with a firm in Sialkot that we discovered that the cutting pattern is well known to mathematicians. The pattern appeared in
a 1990 paper in Discrete & Computational Geometry (Kuperberg
and Kuperberg 1990).17 It also appears, conveniently enough, in
the Wikipedia “Pentagon” entry (Online Appendix Figure A.9).
The pentagons in the new die are offset, with the two leftmost
pentagons sharing half an edge, rather than a full edge. For this
reason, we refer to the new die as the “offset” die, and treat other
dies with pentagons sharing half an edge as variations on our
technology. A two-pentagon variant of our design can be made
using the specifications in the blueprint (with the two leftmost
the largest firms in Sialkot have not allowed us to see their production processes.
Because these two firms are known to produce for Adidas, we suspect that they
were aware of the offset cutting pattern before we arrived. What is clear, however,
is that neither the offset cutting pattern nor the offset die were in use in any other
firm we visited as of the beginning of our experiment in May 2012.
17. The cutting pattern represents the densest known packing of regular pentagons into a plane. Kuperberg and Kuperberg (1990) conjecture that the pattern
represents the densest possible packing, but this is not a theorem.
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1115
FIGURE IV
Cutting Pattern and Blueprint for Offset Four-Pentagon Die
Left figure displays the cutting pattern for the four-panel offset die. Right figure
displays blueprint of the four-panel offset die that was provided to tech-drop firms.
The blueprint contained instructions for modifying the size of the die.
and two rightmost pentagons in the blueprint on the right side
of Figure IV cut separately). This version is easier to maneuver
with one hand and can be used with the same cutting rhythm as
the traditional two-pentagon die. This version has proven more
popular with firms.
II.D. Benefits and Costs
To quantify the various components of benefits and costs of
using the offset die, we draw on several rounds of survey data that
we describe in more detail in Section III. We start by comparing
the typical number of pentagons per sheet using the traditional
die with the number using the offset die. The dimensions of pentagons and hexagons vary slightly across orders, even for balls
of a given official size (e.g., size 5, the standard size for adults),
depending on the thickness and quality of the laminated rexine
sheet used. The most commonly used pentagons have edge length
43.5 mm, 43.75 mm, 44 mm, or 44.25 mm after stitching. The
first two columns of Table I report the means and standard deviations of the numbers of pentagons per sheet for each size, using
a standard (39 in by 54 in) sheet of laminated rexine. Column
(1) uses information from owner self-reports; we elicited the information in more than one round, and here we pool observations
across rounds. Column (2) reports direct observations by our survey team, during the implementation of our first experiment. In
1116
QUARTERLY JOURNAL OF ECONOMICS
TABLE I
PENTAGONS PER SHEET
Traditional die
Size 43.5
Size 43.75
Size 44
Size 44.25
Rescaled (to size 44)
N (all sizes)
Offset die
Owner report
(1)
Direct obs.
(2)
Owner report
(3)
Direct obs.
(4)
257.3
(10.6)
256.3
(6.7)
254.3
(9.1)
246.1
(8.3)
254.2
(8.9)
320
257.7
(6.7)
254.4
(9.4)
248.4
(18.7)
262.0
273.5
(4.4)
269.0
(1.4)
280.0
277.5
(5.3)
272.0
(0.0)
272.5
(0.7)
248.3
(11.0)
39
280.0
(3.0)
8
272.0
275.4
(4.8)
10
Notes. Table reports average (nondefective) pentagons per sheet by die size. Column (1) indicates selfreported numbers from the owner, several rounds per firm in some cases. Column (2) indicates pentagons per
sheet directly observed by the survey team for tech-drop firms (during the initial cutting demonstration) and
for cash-drop firms (at the time of the cash drop). Columns (3) and (4) report numbers for the offset die and
were only collected from tech-drop firms. In the fifth row, pentagons per sheet are rescaled using means for
each size in each column. The final row reports the pooled number of observations for all die sizes. Standard
deviations (when more than one observation available) in parentheses.
the fifth row, we have multiplied the raw measures by the ratio
of means for size 44 mm and the corresponding size; the rescaled
measure provides an estimate of the number of pentagons per
sheet the firm would obtain using a 44 mm die. The owner reports
and direct observations correspond reasonably closely, with owners slightly overestimating pentagons per sheet relative to our
direct observations. Both measures suggest that cutters obtain
approximately 250 pentagons per sheet using the traditional die.
Using the offset die and cutting 44 mm pentagons, it is
possible to achieve 272 pentagons per sheet, as illustrated in
Figure IV.18 For 43.5 mm pentagons, it is possible to achieve 280
pentagons. Columns (3) and (4) of Table I report the means and
standard deviations of pentagons per sheet using the offset die. As
discussed in more detail below, relatively few firms have adopted
the offset die, and therefore we have few observations. But even
with this caveat, we can say with a high level of confidence that
18. If a cutter reduces the margin between cuts, or if the laminated rexine
sheet is slightly larger than 39 in by 54 in, it is possible to cut more than 272
pentagons with a size 44 mm die.
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1117
more pentagons can be obtained per sheet using the offset die. The
directly observed (rescaled) mean is 275.4, and the standard errors indicate that the difference from the mean for the traditional
die (either owner reports or direct observations) is significant at
greater than the 99% level.
To convert these figures into cost savings, we need to know
the proportion of costs that materials and cutting labor account
for. Online Appendix Table A.1 provides a cost breakdown for a
promotional ball obtained from our baseline survey.19 The table
shows that the laminated rexine (rexine plus cotton/polyester plus
latex) accounts for roughly half of the unit cost of production: 46%
on average. The inflatable bladder is the second most important
material input, accounting for 21% of the unit cost. Labor of all
types accounts for 28%, but labor for cutting makes up less than
1% of the unit cost. In the second column, we report the input cost
in rupees; the mean cost of a two-layer promotional ball is Rs 211
(US$2.11).20
The cost savings from the offset die vary across firms, depending in part on the type of rexine used and the number of layers
of cloth glued to it, which themselves depend on a firm’s mix of
promotional, training and match balls. In Table II, we present estimates of the distributions of the benefits and costs of adopting
the offset die for firms. Not all firms were willing to provide a cost
breakdown by input in the baseline survey, and only a subset of
firms have adopted the offset die. To compute the distributions, we
adopt a hot-deck imputation procedure that replaces a firm’s missing value for a particular cost component with a draw from the
empirical distribution (i.e., nonmissing values) within the firm’s
size stratum.21 Following Andridge and Little (2010), we bootstrap
19. In the baseline survey, firms were asked for a cost breakdown of a size 5
promotional ball with two layers (one cotton and one polyester), the rexine they
most commonly use on a two-layer size 5 promotional ball, a glue composed of 50%
latex and 50% chemical substitute (a cheaper alternative), and a 60–65 g inflatable
latex bladder.
20. The exchange rate has varied from 90 Rs/US$ to 105 Rs/US$ over the
period of the study. To make calculations easy, we use an exchange rate of
100 Rs/US$ throughout the article.
21. As discussed below, in our first experiment firms were stratified according
to total monthly output (measured in number of balls) at baseline. One stratum
(the “late responder” stratum) is composed of firms that did not respond to the
baseline survey. Because information on laminated rexine shares was collected
only at baseline, we draw laminated rexine shares for late responders from the
empirical distribution that pools the other strata. (We do not pool for the other
variables, for which we have information on the late responders from later rounds.)
1118
QUARTERLY JOURNAL OF ECONOMICS
TABLE II
BENEFITS FROM ADOPTING THE OFFSET DIE
10th
25th
50th
75th
Panel A: Variable cost reduction from reduced waste of material
Reduction in pentagon
4.24
5.56
6.76
8.02
laminated rexine cost (%) (0.99) (0.85) (0.59)
(1.59)
90th
Mean
10.15
(2.10)
6.93
(0.70)
Laminated rexine as
share of total cost (%)
34.51 39.80 44.83
(3.06) (1.86) (1.94)
51.30
(1.72)
56.09
(3.84)
45.82
(1.58)
Variable cost reduction (%)
0.59
0.77
0.98
(0.11) (0.10) (0.09)
1.24
(0.19)
1.62
(0.27)
1.05
(0.11)
Panel B: Variable cost increase from increased labor time
Cutter wage as share
0.30
0.36
0.45
0.59
of cost (%)
(0.03) (0.01) (0.02)
(0.03)
0.71
(0.04)
0.48
(0.02)
Variable cost increase (%)
0.10
0.12
0.15
(0.01) (0.00) (0.01)
0.19
(0.01)
0.23
(0.01)
0.16
(0.01)
0.42
0.61
0.82
(0.11) (0.10) (0.09)
1.09
(0.19)
1.47
(0.27)
0.89
(0.11)
% net variable cost
red./avg % profit rate
4.55
6.82 10.63
(1.05) (1.13) (1.60)
16.56
(2.35)
24.42
(4.15)
13.07
(1.79)
Total cost savings
per month (Rs 000s)
3.66
9.82 41.35 135.92 397.95 137.77
(0.99) (2.33) (9.43) (36.39) (130.62) (31.68)
Total cost savings per
cutter per month
(Rs 000s)
Days to recover fixed costs
2.75
6.47 14.91
(0.83) (1.33) (2.43)
Panel C: Net benefits
Net variable cost
reduction (%)
Days to recover fixed
costs (no die)
33.83
(6.28)
63.61
(14.02)
27.31
(5.04)
10.28 19.11 43.03 100.86
(2.23) (3.66) (7.37) (21.74)
247.53 168.80
(76.42) (84.72)
5.34
9.92 22.34 52.37
(1.16) (1.90) (3.83) (11.29)
128.53
(39.68)
87.64
(43.99)
Notes. Each row reports the distribution across firms of indicated variable. First row: percentage reduction
in per pentagon laminated rexine cost from adopting the offset die. Second row: laminated rexine cost as a
percentage of total unit costs. Third row: variable cost reduction due to reduced waste of material, computed as
product of reduction in per pentagon laminated rexine cost times 33% (share of pentagons relative to hexagons
in total laminated rexine costs) times laminated rexine’s share of total cost. Fourth row: cutter’s wage as a
share of unit costs. Fifth row: variable cost increase due to increased labor time, calculated as cutter wage
share of cost times 33% (pentagon share of total laminated rexine cost) times 100% (conservative assumption
of increase in cutting time). Sixth row: net variable cost reduction, that is, difference between variable cost
reduction due to reduced wages and variable cost increase due to increased labor time. Seventh row: ratio of
the net variable cost reduction to firm’s average profit margin. (The firm’s profit margin is a weighted average
of its reported profit margin on promotional, training and match balls where the weights are the share of
each ball type in total production.) Eighth row: total cost savings per month in rupees (exchange rate Rs 100
= US$1). Ninth row: cost savings per month per cutter in rupees. Tenth row: days needed to recover all fixed
costs of adoption. Eleventh row: days needed to recover fixed costs of adoption, excluding purchase of the die
(relevant for tech-drop firms which received the die for free). Table uses hot-deck imputation procedure to
replace missing values with draws from distribution of nonmissing values for firm’s stratum. Entire procedure
bootstrapped 1,000 times; quantiles and mean of each variable calculated for each iteration. Table reports
mean and standard deviation for each statistic and variable.
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1119
this entire procedure 1,000 times and calculate the quantiles and
mean of each variable in each iteration, with the table reporting
the mean and standard deviation for each statistic over the 1,000
iterations.
In the first row of Table II, we report the distribution of the
percentage reduction in the cost of laminated rexine used to cut
pentagons when moving to the offset die. The pentagon cost reduction is 6.76% at the median and ranges from 4.24% at the 10th
percentile to 10.15% at the 90th percentile. Combining these figures with the laminated rexine share of total unit costs (which
has the distribution reported in the second row) and multiplying
by 33% (the share of pentagons in total laminated rexine costs,
since a standard ball uses more hexagons than pentagons and the
hexagons are bigger) yields the percentage reduction in variable
material costs reported in the third row. The reduction in variable
material costs is 0.98% at the median and ranges from 0.59% at
the 10th percentile to 1.62% at the 90th percentile.22
The offset die requires cutters to be more careful in the placement of the die when cutting, at least while they are learning how
to use it. A conservative estimate of the increase in labor time
for cutters (for the preferred two-pentagon variant) is 100%.23 (In
Section VI.B we discuss why the 100% number is very conservative.) If firms compensate workers for this extra labor time, labor
costs will increase. The fourth row of Table II reports the distribution of the cutter’s wage as a share of unit costs across firms.
As noted earlier, the cutter’s share of cost is quite low.24 Multiplying the cutter share by 33% (assuming that pentagons take up
22. Note that because a firm at a given percentile of the distribution of rexine
cost reductions is not necessarily the same as the firm at that percentile of the
distribution of rexine as a share of cost, the numbers should not be multiplied
across rows for a particular percentile.
23. As noted, the two-pentagon version of the offset die has proven more
popular with firms. As discussed in more detail in Section IV, we offered firms the
ability to trade in the four-pentagon die, and all firms that traded in requested
the less expensive two-pentagon version. This suggests that any potential speed
benefits from cutting four pentagons at a time with the four pentagon die are
negated by the fact that it is heavier and more difficult to maneuver.
24. The cutter wage as a share of costs reported here is lower than in Online
Appendix Table A.1 because that table reports input components as a share of
the cost of a promotional ball. In Table II, we explicitly account for firms’ product
mixes across promotional, training, and match balls. To get the firm’s average ball
cost, we divide its reported price by 1 plus its reported profit margin for each ball
type and then construct a weighted-average unit cost using output weights for
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QUARTERLY JOURNAL OF ECONOMICS
one third of cutting time, equivalent to their share of laminated
rexine cost) and then by 100% (our conservative estimate of the
increase in labor time) yields the percentage increase in variable
labor costs from adopting the offset die if the firms were to compensate workers (fifth row). Although the offset die is slower and
requires more care to use, our surveys suggest that firms were not
concerned that adoption would lead them to miss more deadlines
or increase defect rates.25
Although the proportional increase in cutting time is potentially large, the cutter’s share of costs is sufficiently low that the
variable labor cost increase is very small. The sixth row reports
the net variable cost reduction, the difference between the variable materials cost reduction and the variable labor cost increase.
The net variable cost reduction is 0.82% at the median, and ranges
from 0.42% at the 10th percentile to 1.47% at the 90th percentile.
Although these numbers are small in absolute terms, the cost
reductions are not trivial given the low profit margins in the industry: 8.39% at the median and 8.42% at the mean. The seventh
row shows the ratio of the net variable cost reductions to average
profits; the mean and median ratios are 13.07% and 10.63%, respectively. If we multiply the net variable cost reduction by total
monthly output, we obtain the total monthly savings, in rupees,
from adopting the offset die (eighth row). The large variation in
output across firms induces a high degree of heterogeneity in total
monthly cost savings. The mean and median monthly cost savings
are Rs 137,770 (US$273) and Rs 41,350 (US$413), respectively,
and savings range from Rs 3,660 (US$37) at the 10th percentile
to Rs 397,950 (US$3,980) at the 90th percentile.
each type. The cutter share of cost is calculated as the per-ball payment divided
by this weighted-average unit cost.
25. After the endline survey, we conducted a short survey on firms in experiment 2 (see Section VI) to collect information on deadlines and defect rates. As
shown in Online Appendix Table A.2, Panel A, defect rates for the traditional
and offset dies (reported by adopters) are virtually identical. Moreover, firms did
not appear concerned about missing orders prior to adopting the die, nor did any
adopter report missing an order deadline because the offset die was too slow. The
table further reveals that the cutting stage is not a bottleneck in the production
process (Online Appendix Table A.2, Panel B). At full capacity, the median firm
only requires 20 days a month of cutting. Firms easily adjust to reach full capacity,
or to meet tight deadlines, by expanding both the number of cutters and the hours
each cutter works. Moreover, most firms can find an additional cutter within a
day or less. Taken together, this evidence indicates that firms were not concerned
about missing deadlines if they adopted the offset die.
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1121
These reductions in variable cost must be compared with the
fixed costs of adopting the offset die. There are a number of such
costs, but they are modest in monetary terms. The market price
for a two-pentagon offset die is approximately Rs 10,000 ($100). As
we explain below, we paid this fixed cost for the firms in the techdrop group. The offset die requires few changes to other aspects
of the production process, since the pentagons it cuts are identical
to the pentagons cut by the traditional die, but there are two
adjustments that many firms make, related to the fact that pairs of
pentagons cut by the offset die remain attached in a different way
(i.e., sharing half an edge) than those cut by the traditional die.
First, for balls with printed pentagons the printing screens must
be redesigned and remade to match the offset pattern. Designers
typically charge Rs 600 (US$6) for a new design; for the minority of
firms that do not have in-house screenmaking capabilities, a new
screen costs Rs 200 (US$2) from an outside screenmaker. (In any
case, new screens must be made for any new order, but we include
them to be conservative.) Second, some firms use a “combing”
machine, a device to enlarge the holes at the edges of panels made
by the cutting die to further facilitate sewing. These machines also
use dies. A two-pentagon combing die that works with pentagons
cut by the two-pentagon offset die costs approximately Rs 10,000
(US$100). For both printing and combing, it is always possible to
cut and work with separate pentagons, but there is a speed benefit
to keeping the pairs of pentagons attached. Adding together the
cost of the die, the cost of a new screen design and screen, and a
new combing die (that not all firms use), a conservative estimate
of total fixed costs of adoption is Rs 20,800 (US$208).
A common way for firms to make calculations about the desirability of adoption is to use a rule of thumb (or “hurdle”) for
the length of time required to recoup the fixed costs of adoption
(the “payback period”). Reviewing a variety of studies from the
United States and United Kingdom, Lefley (1996) reports that
the “hurdles” vary from two to four years, whereas Anderson and
Newell (2004) infer from energy-efficiency audits in the United
States that firms are using hurdles of one to two years. The final
two rows of Table II report the distribution of the number of days
needed to recover the fixed costs of adoption detailed above. For
this calculation, we calculate output per cutter per month and
hence the cost savings per cutter per month (ninth row). Dividing
our conservative estimate of per cutter fixed costs (assuming that
each cutter needs his own offset die and combing die) by the cost
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QUARTERLY JOURNAL OF ECONOMICS
savings per cutter gives the number of days needed to recoup the
fixed costs, reported in the 10th row. The median firm can recover
all fixed costs within 43 days; the payback period ranges from
10 days at the 10th percentile to 248 days at the 90th percentile
(which corresponds to firms that produce very few balls). The final row reports the distribution of days to recover fixed costs that
exclude the cost of purchasing the die; this row is relevant for the
tech-drop firms, to which we gave dies at no cost. In this scenario,
the median time to recover fixed costs is only 22 days, and threequarters of firms can recover the fixed costs within eight weeks.
In short, the available evidence indicates that for almost all firms,
assuming that they are not extraordinarily myopic, there are clear
net benefits to adopting the offset die.
III. DATA AND SUMMARY STATISTICS
Between September and November 2011, we conducted a listing exercise of soccer-ball producers within Sialkot. We found 157
producers that we believed were active in the sense that they had
produced soccer balls in the previous 12 months and cut their own
laminated rexine. Of the 157 firms on our initial list, we subsequently discovered that 22 were not active by our definition. Of
the remaining 135 firms, 3 served as pilot firms for testing our
technology.
We carried out a baseline survey between January and June
2012. Of the 132 active nonpilot firms, 85 answered the survey;
we refer to them as the “initial responder” sample. The low response rate was in part due to negative experiences with previous surveyors. (In the mid-1990s, there was a child-labor scandal in the industry in Sialkot. Firm owners were initially quite
distrustful of us in part for that reason.) In subsequent survey
rounds, our reputation in Sialkot improved and we were able to
collect information from an additional 31 of the 47 nonresponding
producers (the “late responder” sample), to bring the total number of responders to 116. The baseline survey collected firm and
owner characteristics, standard performance variables (e.g., output, employment, prices, product mix, and inputs) and information about firms’ networks (supplier, family, employee, and business networks). To date, we have conducted seven subsequent
survey rounds: July 2012, October 2012, January 2013, March–
April 2013, September–November 2013, January–March 2014,
and October–December 2014. The follow-up surveys have again
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1123
collected information on the various performance measures and
information pertinent to the adoption of the new cutting technology. In tech-drop firms, we have explicitly asked about usage of
the offset die. For the other groups, we have sought to determine
whether firms are using the offset die without explicitly mentioning it, by probing indirectly about changes in the factory. In addition, we have obtained reports of sales of the offset dies from the
six diemakers operating in Sialkot. We have detailed information
on the dates firms have ordered offset dies from the diemakers,
the dates the diemakers delivered the dies, and the number of
offset dies purchased. Based on this information, we believe that
we have complete knowledge of offset dies purchased in Sialkot,
even by firms that have never responded to any of our surveys.
We face three important choices about how to measure adoption. One is whether to impose a lower bound on the number of
balls produced with the offset die. Several firms have reported
that they have experimented with the die but have not actually
used it for a client’s order. To be conservative, we have chosen
not to count such firms as adopters. Our preferred measure of
adoption requires that firms have produced at least 1,000 balls
with the offset die. The measure is not particularly sensitive to
the lower bound; we perform robustness checks using different
cutoffs in Section VI.B. (See note 45.) A second decision we face
is how to deal with the volatility of orders and output in the industry. Many of the smaller and medium-sized firms have large
orders some months and no orders in others. In addition, a particular offset die may not be useful on all orders. Ball designs
and pentagon/hexagon sizes requested by clients vary, and clients
have been known to request that firms use exactly the same dies
as on previous orders. For these reasons, we consider the number
of balls ever produced (rather than produced in the past month)
with the offset die when constructing the adoption measure. The
third choice we face is whether to use information we gained from
firms outside of the formal survey process. Because we have been
concerned about recall bias over periods of more than one month,
our surveys have asked firms about their production in the previous month. However, our project team has been in regular, ongoing
contact with firms, including between survey rounds, and has submitted field notes that summarize these interactions. In one case
in particular, the firm told our enumerators that it had produced
more than 1,000 balls using the offset die, but that order did not
happen to fall in the month preceding a survey wave. To address
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QUARTERLY JOURNAL OF ECONOMICS
this issue, we construct two measures of adoption: a conservative
measure that classifies adoption using only survey data, and a
liberal measure that classifies adoption using both survey data
and field reports. Our preferred measure uses the liberal definition because it incorporates all the information we have regarding
firms’ activities, but we report results for both measures.
Table III presents summary statistics on various firm characteristics, including means and values at several quantiles. Panel
A reports statistics for the sample of 85 initial responders and
Panel B for the full sample that also includes the 31 late responders. Because the late responders did not respond to the baseline,
we have a smaller set of variables for the full sample. Because
firms’ responses are often noisy, where possible we have taken
within-firm averages across all survey rounds for which we have
responses (indicated by “Avg ...” at the beginning of variable names
in the table). Focusing on the initial-responder sample where we
have more complete data, a number of facts are worth emphasizing. The median firm is medium-size (18.3 employees, producing 10,000 balls/month), but there are also some very large firms
(the highest reported employment and production are 1,700 people and 275,000 balls a month, respectively).26 Profit rates vary
across ball types but are generally low, approximately 8% at the
median and 12% at the 90th percentile. (For further discussion
of the heterogeneity in profit rates [mark-ups] across firms, see
Atkin et al. 2015.) The corresponding firm size and profit margins in the full sample (Panel B) are slightly larger, indicating
that the late responders are larger and more profitable than the
initial responders. For most firms, all or nearly all of their production of size 5 balls uses the standard 20 hexagon–12 pentagon
design. The industry is relatively mature; firm age is 19.5 years
at the median and 54 years at the 90th percentile. Finally, cutters
tend to have high tenure; the mean tenure in the current firm
for a head cutter is 11 years (9 years at the median). One other
salient fact is that the vast majority of firms pay pure piece rates
to their cutters and printers. Among the initial responders, 79
of 85 firms pay a piece rate to their cutters, with the remainder
26. The employment numbers understate the true size of firms since the most
labor-intensive stage of production, stitching, is often done outside of firms in
stitching centers or homes.
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1125
TABLE III
FIRM CHARACTERISTICS BY QUANTILE
Mean Min
10th 25th 50th 75th 90th
Panel A: Initial responder sample
Avg output/
31.2
0.8
1.5
3.2 10.0 34.1
month (000s)
Avg employment
89.8
4.0
5.6
7.4 18.3 50.0
Avg employment
5.6
0.7
1.0
1.0
2.2
5.0
(cutters)
Avg Rs/ball
1.5
1.0
1.2
1.3
1.5
1.6
(head cutter)
Avg % size 5
89.4 52.4 68.1 84.0 94.0 100.0
Avg % size 4
3.1
0.0
0.0
0.0
0.0
4.1
Avg % size other
7.5
0.0
0.0
0.0
1.7 11.2
Avg % promotional 41.4
0.0
2.0 18.8 41.1 62.4
(of size 5)
Avg price, size 5
243.1 152.5 185.0 200.0 231.2 269.1
promotional
Avg price, size 5
441.3 200.0 275.0 316.7 392.5 490.0
training
Avg profit %,
8.1
2.6
3.9
5.0
7.9 10.2
size 5 promo
Avg profit %, size 5
7.9
1.6
3.2
4.6
7.8
9.9
training
Avg % lamination
95.7 31.2 81.2 100.0 100.0 100.0
in-house
% standard design 90.7
0.0 70.0 85.0 100.0 100.0
(of size 5)
Age of firm
25.4
2.0
6.0 12.0 19.5 36.5
CEO experience
17.0
3.0
6.0
9.0 15.5 22.0
Head cutter
20.5
2.0
8.0 12.0 18.5 26.5
experience
Head cutter
11.1
0.0
2.0
6.0
9.0 15.0
tenure
Panel B: Full sample
Avg output/
33.4
month (000s)
Avg employment
103.2
Avg employment
5.2
(cutters)
Avg Rs/ball
1.6
(head cutter)
Max
N
275.0
85
235.0 1,700.0
12.0 123.0
85
85
71.7
1.9
2.9
79
100.0
7.8
20.0
80.0
100.0
35.0
47.6
100.0
85
85
85
85
300.0
575.0
64
600.0 2,250.0
73
12.5
20.0
64
11.8
22.2
71
100.0
100.0
75
100.0
100.0
80
54.0
28.0
41.0
108.0
66.0
46.0
84
82
36
22.0
46.0
35
0.0
1.6
4.6
15.2
37.0
4.0
0.7
6.0
1.0
8.2
1.2
24.9
2.4
75.0 227.0 2,180.0 115
5.0 12.0 123.0 115
1.0
1.1
1.3
1.5
1.7
86.2
2.1
275.0 116
3.0 108
1126
QUARTERLY JOURNAL OF ECONOMICS
TABLE III
(CONTINUED)
Mean Min
10th 25th 50th 75th 90th
Avg % size 5
88.6 42.8 64.2 83.3 94.4
Avg % size 4
2.5
0.0
0.0
0.0
0.0
Avg % size other
8.9
0.0
0.0
0.0
2.2
Avg % promotional 37.0
0.0
0.0
8.3 33.8
(of size 5)
Avg price, size 5
248.6 150.0 185.0 205.0 236.7
promotional
Avg price, size 5
465.3 200.0 300.0 335.2 400.0
training
Avg profit (%),
8.1
2.6
3.9
5.0
7.4
size 5 promo
Avg profit (%),
8.1
1.6
3.2
5.0
8.0
size 5 training
Avg % lamination
96.2 25.0 85.0 100.0 100.0
in-house
Max
N
100.0 100.0
3.3
6.0
13.3 31.9
55.2 80.0
100.0
35.0
57.2
100.0
115
115
115
114
270.0 310.0
575.0
83
508.2 662.5 2,250.0 102
10.4
13.6
20.0
82
10.0
13.0
22.2
98
100.0 100.0
100.0 104
Notes. Variables beginning with “Avg” represent within-firm averages across all rounds for which responses
are available. Initial responder sample contains firms that responded to baseline survey. Piece rate and prices
are in rupees (exchange rate 100 Rs = US$1). Size 5 is regulation size for adults; size 4 is commonly used by
children; Avg % size other refers to sizes 1, 2, and 3. % standard design (of size 5) refers to percentage of size
5 balls produced with standard 20 hexagon–12 pentagon design. Age, experience, and tenure in years. Firms
with missing values in all rounds in which questions were asked are dropped (hence the variation in N in
final column).
paying a daily, weekly, or monthly salary sometimes accompanied
by performance bonuses.27
IV. EXPERIMENT 1: THE TECHNOLOGY DROP
Here we briefly describe our first experiment, the technology
drop. For the purposes of the current article, the first experiment
mainly serves to provide evidence of low adoption, a puzzle we
investigate using our second experiment, motivated in Section V
and described in Section VI. As mentioned in the introduction, we
originally planned to focus on spillovers of the technology to nontech-drop firms. This partly explains why we treated a relatively
small proportion of firms in the first experiment. We are planning
to focus on spillovers in a companion project.28
27. In a later survey round, we found that more than 90% of firms pay their
printers a piece rate.
28. Spillovers are thought to be a key mechanism generating increasing returns, and they also provide the primary economic rationale for industrial policies to increase investment in innovation. To investigate such spillovers and
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1127
The 85 firms in the initial-responder sample were divided
into four strata based on quartiles of the number of balls produced in a normal month from the baseline survey. Within these
strata firms were randomly assigned to one of three groups: the
tech-drop group, the cash-drop group, and the no-drop group.
We included the cash-drop group to shed light on the possible
role of credit constraints in the technology-adoption decision.29
The top panel of Table IV summarizes the distribution of firms
across groups for the initial responder sample.30 To increase
sample size, we also randomized the initial nonresponders into
three groups using the same proportions as for the initial responders (treating them as a separate stratum). The bottom panel of
Table IV summarizes the response rates for the initial nonresponders. It is important to note that response rates of the active initial
nonresponders are clearly correlated with treatment assignment:
firms assigned to the tech-drop and cash-drop groups were more
likely to respond than firms assigned to the no-drop group. For
this reason, when it is important that assignment to treatment
in the tech-drop experiment be exogenous, we focus on the initial
their pathways, we collected detailed data on firm networks, as mentioned in
Section III. We chose the number of firms to be treated based on calculations for
statistical power to pick up spillover effects. We are continuing to track information flows and adoption by non-tech-drop firms, and are planning to analyze the
patterns in the companion project. But given the low adoption rates among the
tech-drop firms—and hence the very limited adoption among firms who did not receive the technology—we decided to focus first on the puzzle of low take-up among
the firms we gave the technology to. We chose not to do the second experiment on
the non-tech-drop firms (cash-drop and no-drop groups) from the first experiment
for the reasons discussed in note 44. There are natural synergies between the
two projects because in the presence of misaligned incentives, we would predict
that spillovers disproportionately originate from firms with better alignment of
incentives.
29. In an experiment with micro-enterprises in Sri Lanka, de Mel, McKenzie,
and Woodruff (2008) find very high returns—higher than going interest rates—to
drops of cash of US$100 or US$200 (or of capital of roughly similar magnitudes),
suggesting that the micro-enterprises operate under credit constraints. Although
our prior was that the cash value of the offset die would matter less to the larger
firms in our sample, we included the cash-drop component to be able to separate
the effect of the shock to capital from the effect of knowledge about the technology.
30. At the moment of assignment, we believed that there were 88 active initialresponder firms with 22 in each stratum. In each stratum, 6 firms were assigned
to the tech-drop group, 3 to cash-drop group, and 13 to the no-drop group. Three
firms that responded to our baseline survey subsequently either shut down or were
revealed not to be firms by our definition, leaving 85 firms.
1128
QUARTERLY JOURNAL OF ECONOMICS
TABLE IV
TREATMENT ASSIGNMENT, TECH-DROP EXPERIMENT
# Firms
Tech drop Cash drop No drop
Total
Panel A: Initial responders
Smallest
Medium-small
Medium-large
Largest
Total
5
6
6
6
23
3
3
3
3
12
12
13
13
12
50
20
22
22
21
85
Panel B: Initial nonresponders
Active, late response
Active, refused all surveys
Inactive (revealed not to be a producer)
12
0
7
5
1
3
14
15
12
31
16
22
Notes. Table reports numbers of firms by treatment assignment among initial responders (Panel A) and
initial nonresponders (Panel B). Active firms are those who had produced soccer balls in the previous 12 months
and cut their own laminated rexine. Last row reports numbers of firms included in initial randomization that
were believed to be active (based on an initial listing exercise) but were later revealed not to be active by our
definition because they had shifted entirely to other products, had gone out of business, or were not cutting
their own laminated rexine.
responder sample. In our second experiment, where we focus only
on active tech-drop firms, all of which responded, this distinction
will not be important.
We began the technology-drop experiment in May 2012. Firms
assigned to the technology group were given a four-pentagon offset die, along with a blueprint that could be used to modify the
die (Figure IV). They were also given a 30-minute demonstration,
which involved first watching the firm’s cutter cut a sheet using
the traditional die and counting the number of panels, then instructing the cutter how to cut using the offset die and counting
the panels. We continued the demonstration until the cutter was
able to cut 272 pentagons from a sheet. In almost all cases, this
required one or two sheets; in no case did it require more than
three. The die we provided cuts pentagons with edge length of
44 mm. As noted in Section II, firms often use slightly different
size dies, and the pentagon die size must match the hexagon die
size. For this reason, we also offered firms a free trade-in: we
offered to replace the die we gave them with an offset die of a
different size, produced by a local diemaker of their choice. Firms
could also trade in the four-panel offset die we gave them for a
two-panel offset die (of the same or a different size). Of the 35
tech-drop firms, 19 took up the trade-in offer. All chose to trade-in
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1129
for the two-panel version. The cash group was given cash equal
to the price we paid for each four-pentagon offset die, Rs 30,000
(US$300), but no information about the offset die. Firms in the
no-drop group were given nothing.
To examine baseline balance, Panel A of Online Appendix
Table A.3 reports the mean of various firm characteristics across
the tech-drop, cash-drop and no-drop groups for the initialresponder sample. We find no significant differences across
groups. Panel B of Online Appendix Table A.3 reports the analog
for the 31 late responders. Here we see significant differences for
various variables, consistent with the observation that response
rates among this group are endogenous to treatment assignment.
Table V reports adoption rates as of August 2013, 15 months
after we introduced the technology, with the initial-responder
sample in Panel A and the full sample in Panel B. The first three
rows of each panel indicate the number of firms that were both
active and responded to our surveys. The fourth row shows that a
high proportion of tech-drop firms took up our offer of a trade-in
for a different die, as mentioned already. The fifth and sixth rows
report the number of firms that ordered and that received dies
(beyond the one trade-in offered to tech-drop firms). The numbers
are modest: in the full sample, one tech-drop firm and six no-drop
firms made an additional order. (One diemaker was slow in delivering dies and firms canceled their orders, hence the discrepancy
between the fifth and sixth rows.) The seventh and eighth rows
report adoption as of August 2013 using the conservative and liberal adoption measures discussed in Section III. Using the liberal
adoption variable, in the full sample there were five adopters in
the tech-drop group and one in the no-drop group. (In the initialresponder sample, the corresponding numbers are four and zero.)
This number of adopters struck us as small. Given the apparently
clear advantages of the technology, we were expecting much faster
take-up among the firms in the tech-drop group.
We investigated several alternative hypotheses for the low
take-up, but found little evidence for the most common existing
explanations. Lack of awareness of the technology (the assumption underlying “epidemic” models of diffusion, one of two main
categories reviewed by Geroski 2000) cannot be the explanation
among tech-drop firms, since we ourselves manipulated the firms’
information set. Another natural hypothesis is simply that the
technology does not reduce variable costs as much as we have argued that it does. One piece of evidence against this hypothesis
1130
QUARTERLY JOURNAL OF ECONOMICS
TABLE V
ADOPTION OF TECHNOLOGY AS OF AUGUST 2013
Tech drop Cash drop No drop
Panel A: Initial-responder sample
# ever active firms
# ever responded
# currently active and ever
responded
# traded in
# ordered offset die (beyond trade-in)
# received offset die
(beyond trade-in)
# ever used offset die
(>1,000 balls, conservative)
# ever used offset die
(>1,000 balls, liberal)
Panel B: Full sample
# ever active firms
# ever responded
# currently active and ever
responded
# traded in
# ordered offset die (beyond trade-in)
# received offset die (beyond
trade-in)
# ever used offset die
(>1,000 balls, conservative)
# ever used offset die
(>1,000 balls, liberal)
Total
23
23
22
12
12
11
50
50
46
85
85
79
15
1
1
0
0
0
0
4
2
15
5
3
3
0
0
3
4
0
0
4
35
35
32
18
17
15
79
64
59
132
116
106
19
1
1
0
0
0
0
6
4
19
7
5
4
0
1
5
5
0
1
6
Notes. Table reports adoption statistics as of August 2013 in the initial-responder sample (Panel A) and the
full sample (Panel B). # ever responded is the number of firms that answered at least one of the surveys across
rounds. Fourth row reports the number of firms that took up the option to trade in the original four-panel offset
die for a different offset die. The discrepancy between the fifth and sixth rows is due to one diemaker being
very slow in delivering offset dies and firms canceling their orders. Seventh row uses conservative definition
of adoption: the number of firms that produced at least 1,000 balls as of August 2013 based on survey data.
Eighth row uses liberal definition, which combines survey data and field reports from our enumerators.
is the revealed preference of the six firms who adopted. Although
these revealed preferences do not indicate the profitability of the
technology among the full distribution of firms (as our calculations in Section II.D do), they are still instructive. In particular,
the one adopter in the no-drop group, which we refer to as Firm
Z, is one of the largest firms in Sialkot. This firm ordered 32
offset dies on nine separate purchasing occasions between May
2012 and August 2013, and has ordered 8 more dies since then.
Online Appendix Figure A.10 plots the timing and quantity of its
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1131
die orders. In March–April 2013 (round 4 of our survey) the firm
reported that it was using the offset die for approximately 50% of
its production and has since reported that the share has risen to
100%. It would be hard to rationalize this behavior if the offset
die were not profitable for this firm.
We have estimated a number of simple linear probability models relating adoption as of August 2013 to measures of scale of
production, quality of output produced, managerial ability, and
employee skill. Scale may be important to allow firms to spread
fixed costs over more units. Quality may matter because the offset die generates greater cost saving for firms using more expensive, high-quality rexine. Manager and worker skill are often
thought to be important for technology adoption. Online Appendix
Tables A.4 and A.5 report the results. They can be briefly summarized: we do not find robust correlations between adoption and
any of the covariates reflecting scale, quality, manager, or employee skill. Given the small number of adopters as of August
2013, it is perhaps not surprising that we have not found robust
correlations with firm characteristics. But we do interpret the results as deepening the mystery of why so few firms adopted the
offset die.
V. THEORY: MOTIVATING EVIDENCE AND SUMMARY
In this section, we first discuss qualitative evidence that motivates our model of strategic communication in a principal-agent
setting (Section V.A). We then summarize the model and discuss
its implications (Section V.B). To save space, the formal exposition
of the model appears in Online Appendix B.
V.A. Motivating Evidence
Puzzled by the lack of adoption, in the March–April 2013 survey round we added a question asking tech-drop group firms to
rank the reasons they had not adopted the new technology, providing nine options (including an “other” category). Table VI reports
the responses for the 18 tech-drop firms that responded. Ten of the
18 firms reported that their primary reason for not adopting was
that their “cutters are unwilling to work with the offset die.” Four
of the 18 said that their primary problem related to “problems
adapting the printing process to match the offset patterns,” and
5 more firms selected this as the second most important barrier
2
2
2
2
2
4
3
3
3
1
1
1
3
3
2
1
5
2
3
2
3
Too
busy
3
2
Doubt
profitable
Waiting for
others to
prove value
Waiting for
others to
iron out kinks
1
1
1
1
1
1
1
1
1
1
Cutters
unwilling
2
2
2
2
2
1
1
1
1
Printing
problems
4
Other
production
issues
3
3
Other
Notes. Table reports responses from the March–April 2013 survey round of the 18 tech-drop firms that responded to the question “Select the main reason(s) why you are not
currently using an offset die. If more than one, please rank those that apply in order.” The nine categories were: (a) “I have not had any orders to try out the offset die.” (b) “I have
been too busy to implement a new technology.” (c) “I do not think the offset die will be profitable to use.” (d) “I am waiting for other firms to adopt first to prove the potential of the
technology.” (e) “I am waiting for other firms to adopt first to iron out any issues with the new technology.” (f) “The cutters are unwilling to work with the offset die.” (g) “I have had
problems adapting the printing process to match the offset patterns.” (h) “There are problems adapting other parts of the production process (excluding printing or cutting problems).”
(i) “Other [fill in reason].”
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Firm
No orders
to try on
TABLE VI
REASONS FOR NONADOPTION (TECH-DROP FIRMS) AS OF AUGUST 2013
1132
QUARTERLY JOURNAL OF ECONOMICS
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1133
to adoption. This issue may be related to the technical problem
of redesigning printing screens, but as noted already, the cost of
a new screen from an outside designer is approximately US$6. It
seems likely that the printing problems were related to resistance
from the printers. (The other popular response to the question,
to which most firms gave lower priority, was that the firm had
received insufficient orders.)
The responses to the survey question were consistent with
anecdotal reports from several firms. One notable piece of evidence is from Firm Z, the large adopter from the no-drop group,
which is an exception to the local norm of piece-rate contracts. In
part because of pressure from an international client, for several
years the firm has instead paid a guaranteed monthly salary supplemented by a performance bonus, to guarantee that all workers
earn at least the legal minimum wage in Pakistan. The fact that
this large early adopter uses an uncommon incentive structure
for cutters and printers seemed very suggestive.
We also feel that it is useful to quote at some length from field
reports submitted by our survey team. To be clear, the following reports are from factory visits during the second experiment, which
is described in Section VI, and we are distorting the chronology of
events by reporting them here. But they are useful to capture the
flavor of the owner–cutter interactions that we seek to capture in
the theoretical model. As described in more detail shortly, in our
second experiment we offered one cutter in each firm (conditional
on the approval of the owner) a lump-sum Rs 15,000 (US$150)
incentive payment to demonstrate competence in using the offset
die. (We also offered an incentive to one printer.) The following
excerpts are all from firms in the group assigned to treatment for
the second experiment.
In one firm, the owner told the survey team that he was willing to participate in the experiment but that the team should ask
the cutter whether he wanted to participate. The report continues:
[The cutter] explained that the owner will not compensate him for
the extra panels he will get out of each sheet. He said that the
incentive offer of [Rs] 15,000 is not worth all the tensions in future.
It appears in this case that the cutter, anticipating that the owner
would not adjust his wage, sought to withhold information about
the offset die to avoid a future decline in his pay. The cutter declined to participate, and the firm was not treated.
1134
QUARTERLY JOURNAL OF ECONOMICS
In another firm, the owner, who had agreed to participate in
the treatment, was skeptical when the enumerators returned to
test the cutter. Our survey team writes,
[The owner] told us that the firm is getting only 2 to 4 extra pentagon
panels by using our offset panel. . . The owner thinks that the cost
savings are not large enough to adopt the offset die. . . He allowed
us to time the cutter.
The team then continued to the cutting room without the owner.
On entering the cutting area, we saw the cutter practicing with our
offset die. . . We tested the cutter. . . He got 279 pentagon pieces in
2 minutes 32 seconds. . . The cutter privately told us that he can
get 10 to 12 pieces extra by using our offset die.
The owner then arrived in the cutting area.
We informed the owner about the cutter’s performance. The owner
asked the cutter how many more pieces he can get by using the
offset die. The cutter replied, “only 2 to 4 extra panels.”
It appears that the cutter had been misinforming the owner. But
the cutter did not hide the performance of the die in the cutting
process itself, likely either because it was difficult to do so or
because he did not want to jeopardize his incentive payment.
The owner asked the cutter to cut a sheet in front of him. The cutter
got 275 pieces in 2 minutes 25 seconds. The owner looked satisfied
by the cutter’s speed. . . The owner requested us to experiment with
volleyball dies.
This firm subsequently adopted the offset die.
In a third firm, the owner reported that he had modified the
wage he pays to his cutter to make up for the slower speed of the
offset die. Our team writes,
[The owner] said that it takes 1 hour for his cutter to cut 25 sheets
with the conventional die. With the offset die it takes his cutter 15
mins more to cut 25 sheets for which he pays him [Rs] 100 extra for
the day which is not a big deal.
This firm has generally not been cooperative in our survey, and
we have not been able to verify that it has produced more than
1,000 balls with the offset die. For this reason it is not classified
as an adopter.
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1135
V.B. Summary of Model
The survey results and qualitative evidence highlight the role
of misaligned incentives and suggest that workers are discouraging some firms from adopting. But this raises two important questions. First, given that owners should be aware that workers have
an incentive to discourage adoption of the offset dies, why are they
influenced by what the workers say? Second, why do owners not
simply offer a different labor contract, to give workers an incentive
to support the adoption of the cost-saving technology? To address
these questions and motivate our second experiment, we develop
a model embedding a cheap-talk interaction along the lines of
Crawford and Sobel (1982) in a standard principal-agent model.
We recognize that other models with misaligned incentives and
an ability of cutters to impose direct costs on firms may generate
similar predictions. (See in particular Dow and Perotti 2013 and
Sections IV and V of Garicano and Rayo 2016.) But in our view the
model provides a particularly parsimonious account of the main
forces at play. (The full exposition is in Online Appendix B.)
We consider a principal (she) and an agent (he) in a onetime interaction. Production technologies are characterized by
marginal cost, c, and speed, s. There is an existing technology,
with c0 and s0 . The principal is aware of the existence of a new
technology, which may be of three types: θ 1 , which has the same
costs as the existing technology but is slower; θ 2 , which is similar
to our technology in that it has lower marginal cost than the existing technology but is slower (with the cost reduction more than
offsetting the increased labor time); and θ 3 , which has the same
marginal costs as the existing technology but is faster. The principal has priors ρ 1 , ρ 2 , and ρ 3 that the technology is of each type.31
In stage 1, the principal must choose the contract she offers
the agent. In stage 2, nature reveals the type of the new technology
to the agent but not the principal. Although this is clearly an
extreme assumption, it captures in an analytically tractable way
the observation from our qualitative work that the cutters are
better informed about the cutting dies (which they work with all
day every day) than are owners. In stage 3, the agent can send
31. We impose restrictions on the parameters of the model such that, for all
the values of the piece rate that will be relevant, the principal, if she knew the
technology type with certainty, would choose to adopt technology types θ 2 and θ 3
and not type θ 1 . In addition, the restrictions imply that the principal would not
adopt the new technology given only her priors.
1136
QUARTERLY JOURNAL OF ECONOMICS
a message to the principal about the technology. In stage 4, the
principal decides whether to adopt. In stage 5, the agent chooses
his level of effort. In stage 6, output is observed, the technology is
revealed to the principal, and payoffs are realized.
We consider two cases of the model, one in which the only
available contract is a standard piece rate that must be chosen in
stage 1 (Section V.B.1), and the other a “conditional” contract in
which the piece rate can be conditioned on marginal cost, which
is only revealed ex post, in stage 6 (Section V.B.2). We discuss
the implications in Section V.B.3 and the relation to our second
experiment in Section V.B.4.
1. No Conditional Contracts. In this case, we assume that
the piece rate cannot be conditioned on information that is revealed later in the game, in particular on marginal cost. Given
this, the agent strictly prefers faster technologies. Hence the principal and agent have a common interest to adopt type θ 3 and not
to adopt type θ 1 but divergent interests over type θ 2 .
The analysis of the model is complicated by the fact that we
have cheap talk embedded in a principal-agent interaction: for
every choice of the piece rate there is a different cheap-talk subgame. But the analysis is greatly simplified by Lemma 1 (Lemma 1
and both propositions appear in Online Appendix B.), which holds
that in any subgame, modulo treating messages that induce the
same action by the principal as equivalent (following Crawford
and Sobel 1982), there are just two possible subgame equilibria:
a “babbling” equilibrium in which the principal ignores what the
agent says and does not adopt; and an “informative” equilibrium
in which the agent seeks to encourage adoption of type θ 3 and
discourage adoption of types θ 1 and θ 2 , and the principal is influenced by the agent’s message. We place no restrictions on the
richness of the space of possible messages, but as shorthand we
refer to a message that encourages the principal to adopt as “technology is good” and one that discourages her as “technology is
bad.” In the informative subgame equilibrium (if it exists), if the
principal hears the message technology is bad, she infers that the
technology is type θ 1 or θ 2 ; if technology is good, she infers θ 3 .
One implication of Lemma 1 is that there cannot exist an equilibrium in which the agent fully reveals the technology type to the
principal.
Our key proposition in this case (Proposition 1) holds that
there exists an equilibrium in which the principal offers the
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1137
same piece rate as under the existing technology, the agent says
technology is bad if it is type θ 1 or θ 2 and technology is good if it is
type θ 3 , and the principal follows the agent’s advice. In particular,
if the technology is type θ 2 the agent discourages the principal
from adopting and she does not adopt.32 It is common in the literature on cheap talk to assume that players will coordinate on the
most informative equilibrium in cheap-talk interactions.33 If we
adopt this assumption (for every cheap-talk subgame), then the
equilibrium described in Proposition 1 is unique.
2. Conditional Contracts. In this case, we allow the principal
at stage 0 to pay a fixed cost, G, to be able to offer a contract that
conditions the piece rate on marginal cost, revealed in stage 6.
Given our assumptions, only type θ 2 differs in marginal cost from
the existing technology. The conditional contract is useful in that it
enables the principal to offer a higher piece rate if the technology
is type θ 2 . The fixed cost G can be interpreted as the cost of a
commitment device to pay a piece rate above the one that would
be paid for the existing technology, which otherwise the principal
would not be able to credibly commit to. (We discuss other possible
interpretations in Section V.B.3.)
Our main proposition in this case (Proposition 2) holds that
there exists an equilibrium in which, if the contracting fixed cost G
is sufficiently small, the principal pays G and offers a supplement
to the piece rate if the technology is revealed to be type θ 2 . Given
the supplement, the agent wants to adopt the same technologies
that the principal wants to adopt, θ 2 or θ 3 , and technologies of
type θ 2 are adopted.
“Sufficiently small” for G means less than the expected benefit of adopting technology θ 2 relative to the existing technology,
32. A word of explanation about whether the equilibrium involves misinformation: formally, the actual words that the agent uses to convey his message are
immaterial; what matters is how the principal interprets the message, and in the
Proposition 1 equilibrium the principal interprets “technology is bad” to mean
“technology is type θ 1 or θ 2 ”. In this sense, the agent technically does not misinform the principal about type θ 2 , and we avoid using this terminology in the theory.
But in the empirical setting we have found that workers tend to use particular
words to discourage adoption that paint the type θ 2 technology as ineffective, and
describing the communication as misinformation there is more apt.
33. Crawford and Sobel (1982) argue that the most informative equilibrium
is “a reasonable one for agents to coordinate on” because it is Pareto-optimal in
their setting. Other justifications for the assumption have also been discussed in
the literature; see, for example, Chen, Kartik, and Sobel (2008).
1138
QUARTERLY JOURNAL OF ECONOMICS
which depends on speed and cost of the technologies as well as
the principal’s prior, ρ 2 , that the technology is type θ 2 . If ρ 2 is sufficiently low, then the principal will not pay G and the players
will be back in the world of the first case, where their interests
diverge and type θ 2 will not be adopted. Again, if we assume players coordinate on the most informative subgame equilibria, then
the Proposition 2 equilibrium is unique.
3. Discussion. Let us come back to the two questions posed
at the beginning of the model summary. First, why are owners
influenced by cutters’ messages, given that they should be aware
that cutters have an incentive to resist adoption of our die? The
answer from the model is that the interests of the principal and
agent are sufficiently aligned—they have common interests over
types θ 1 and θ 3 —that the agent’s message can be informative.
In particular, in the Proposition 1 equilibrium, the principal can
infer whether θ = θ 3 or θ ∈ {θ 1 , θ 2 }, and this is valuable enough
that she pays attention to the agent’s message, knowing that it
may lead her not to adopt type θ 2 .
Second, why don’t owners offer a labor contract that incentivizes cutters to support the adoption of our die? The model suggests two reasons. One is simply that the principal may be unaware of the existence of the conditional contract; this corresponds
to the no-conditional-contracts case. The conditional contract may
be an organizational innovation that was previously unknown, at
least to some firms, in the same way that our technical innovation
was previously unknown.
Another reason, corresponding to the conditional-contracts
case, is that the principal is aware of the conditional contract but
perceives the cost of implementing it to be higher than the expected benefit. The contracting fixed cost, G, can be interpreted in
a number of ways. It may be that a fairness norm has arisen
around standard piece-rate contracts, such that any deviation
would carry a morale cost. Or firms may worry that if they offer a higher piece rate in one case, workers will demand higher
rates in others. G can also be interpreted as a cost of a commitment
device for the principal’s pledge to pay the higher piece rate if the
technology is type θ 2 .34 Finally, the fixed cost can be interpreted
34. In our model, marginal cost is revealed in stage 6, after effort has been
supplied in stage 5. Since the game is one-shot, the principal would have an
incentive not to pay the higher piece rate as promised. (This incentive would be
present under the existing technology as well, but it seems plausible that it would
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1139
in light of the ratchet effect (e.g., Gibbons 1987). If a worker on
a piece-rate contract discovers a labor-saving innovation, he may
not reveal it to the owner if he expects her to cut the piece rate
in response. It may be optimal for the principal to commit to not
changing the piece rate to encourage labor-saving innovations. If
most innovations in Sialkot are labor-saving, this may explain
why piece rates are sticky and why it may be costly for firms to
start offering conditional contracts—contracts that open the door
to ratcheting. In Section VII, we discuss the previous cutting innovation in Sialkot, which was labor-saving. It seems plausible for
firms to expect other new technologies to be labor-saving as well
and to be reluctant to modify piece rates for this reason.
These two possible explanations for the stickiness of labor
contracts—that owners are not aware of the existence of conditional contracts, and that the benefits owners expect do not outweigh the costs of adopting them—have similar implications for
the players’ behavior. In either case, the principal offers a pure
(nonconditional) piece-rate contract and a technology of type θ 2
is not adopted. The key point is that for whatever reason, many
owners did not in fact adjust labor contracts. This left scope for
our incentive intervention, described below, to have an effect.
This discussion is related to a broader debate in organizational economics about the underlying causes of organizational
failure, of which the lack of adoption of our dies is arguably an
example. An organization may fail because it was poorly designed
ex ante. Or its design may represent an ex ante optimal solution
to trade-offs between uncertain costs and benefits, but a shock
yields a suboptimal outcome ex post. In our context, the idea that
an owner is simply not aware of the existence of conditional contracts corresponds to the first view. The idea that the owner perceives the fixed cost of offering a conditional contract to be greater
than the expected benefit of adopting a type θ 2 technology corresponds to the second. Our preferred interpretation is the latter;
we believe that most owners are aware of the possibility of conditional contracts but choose not to offer them because they assign
low probability to the arrival of beneficial new technologies like
our dies. But we recognize that the two interpretations predict
be more difficult for firms to avoid paying the going rate in the industry, which
here corresponds to the optimal piece rate under the existing technology.)
1140
QUARTERLY JOURNAL OF ECONOMICS
similar behavior and it is difficult to discriminate between them
empirically.35
4. Motivation for Incentive Intervention. Testing the implications of our model presents a number of practical challenges.
In principle, one approach would be to pay the fixed cost, G, for
firms and examine whether they change the labor contract and
adopt the technology. But this cost is not observable and depends
on complex social dynamics within firms; it is not clear how much
we (the experimenters) would pay or to whom. Another approach
would be to offer a piece-rate supplement ourselves. But firms
are reluctant to share the detailed production information that
would be required to implement such a payment. The challenges
we faced simply in surveying firms indicated to us that it would
be impossible to manipulate the piece rate directly in our second
experiment.36
Facing these constraints, we opted for a third approach: we offered a one-time lump-sum payment to one cutter and one printer
per firm, conditional on revealing the offset die to be cost-saving in
front of the owner. (Presumably, other nonmonetary inducements,
such as increased authority or greater work schedule flexibility,
could have served a role similar to our one-time lump-sum payment.) In Online Appendix B.4, we prove a formal proposition that
if such a conditional lump-sum payment by a third-party experimenter is sufficiently large, then there exists an equilibrium in
which the agent encourages adoption of type θ 2 and the principal adopts it, even if she would not have offered the conditional
contract on her own. Intuitively, the incentive payment plays the
same role as the supplement to the piece rate in the conditional
contracts case: it raises the payoff to the agent of revealing the
technology to be type θ 2 . Empirically, the main implication is that
we would expect a sufficiently large lump-sum incentive payment
to increase adoption of the offset die. It is worth noting that “sufficiently large” in this context is relative to the possible wage losses
35. A third view should be considered: that the pure (nonconditional) piece
rate is a first-best contract (with no misalignment of incentives) ex ante, and the
misalignment arises only because of the intervening shock. In our model, this can
be interpreted as a limiting case of the second view above, where ex ante the owner
is certain that the technology is not of type θ 2 (that is, ρ 2 = 0).
36. In the end, one-third of firms chose not to participate in the much less
invasive intervention we decided to implement; see Section VI for details. This
confirmed our earlier belief that firms’ willingness to participate would be limited.
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1141
of a single cutter, not to the revenues or profits of a firm. Indeed,
we will see that a lump-sum incentive payment that appears small
from the point of view of firms has a large effect on adoption.37
VI. EXPERIMENT 2: THE INCENTIVE PAYMENT
VI.A. Experimental Design
Motivated by the theoretical model, we conducted our second experiment, the incentive payment, in September–November
2013. To avoid interfering with the process of diffusion to the
non-tech-drop firms that is the focus of our companion project on
spillovers (see note 28), we focused on the 35 tech-drop firms (including both initial responders and initial nonresponders). The
31 still-active firms among these were divided into four similarly
sized strata:38 (i) firms in the two smaller strata from the tech-drop
experiment that had not yet adopted the die as of August 2013;
(ii) firms in the two larger strata from the tech-drop experiment
that had not yet adopted the die; (iii) firms from the initial nonresponder stratum from the tech-drop experiment that had not yet
adopted the die; and (iv) firms that had already adopted the die.39
Within each stratum, firms were randomly assigned with equal
proportion to a treatment subgroup (which we call Group A) and
a control subgroup (Group B). There were 15 firms assigned to
Group A and 16 to Group B.
To firms in Group B we gave a reminder about the offset
die and the new cutting pattern and explicitly informed them
about the two-pentagon variant of the offset die (which, as noted,
had proven more popular than the four-pentagon offset die we
37. Note, however, that the required size of the lump-sum transfer may vary
across workers, depending on individual and firm characteristics. In the first anecdote above, for instance, it appears that the proposed payment was not large
enough to induce the cutter to report accurately. In all other cases it appears that
the proposed transfer was large enough to induce cutters to participate in the
intervention.
38. At the time of randomization, we believed that 34 of these firms were still
active. Three of these were subsequently revealed to have stopped manufacturing
soccer balls.
39. As shown in Table V, there were four adopters as of August 2013 in the
full sample using the conservative definition (which is based on our survey data),
and five adopters according to the liberal definition (which combines survey data
with field reports submitted by our enumerators). We stratified using the liberal
definition, our preferred measure.
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QUARTERLY JOURNAL OF ECONOMICS
originally distributed.) We also offered to do a new demonstration
with their cutters. To each firm in Group A, we gave the same
refresher, the same information about the two-pentagon variant,
and the same offer of a new demonstration. In addition, we explained the misalignment of incentives to the owner and offered to
pay one cutter and one printer lump-sum bonuses roughly equivalent to their monthly incomes—15,000 Rs (US$150) and 12,000 Rs
(US$120), respectively—on the condition that within one month
they demonstrate competence in using the new technology in front
of the owner. If the owner agreed to the intervention, we explained
the intervention to one cutter and one printer chosen by the owner,
paid them one third of the incentive payment on the spot, and
scheduled a time to return to test their performance using the
die.40
The performance target for cutters was 272 pentagons from
a single sheet in three minutes using the offset die. The target for
the printer was 48 pairs of offset pentagons in three minutes.41 We
provided the owner with 20 laminated rexine sheets and printing
screens for offset pentagon pairs for his workers to practice with,
and a nominal Rs 5,000 (US$50) to cover additional costs such
as overhead (e.g., electricity while the cutters were practicing).
We returned after approximately one month to test the employees
and, upon successful achievement of the performance targets, to
pay the remaining two thirds of the incentive payments. Without
revealing ahead of time that we would do so, we allowed for a
buffer of 30 seconds and five pentagons for cutters and 30 seconds
for printers.
Table VII evaluates baseline balance by comparing firm characteristics across Group A and Group B firms at the time of our
visit to explain the intervention (September 2013). No differences
in means are statistically significant. It appears that the randomization was successful.42
40. To the extent possible, we attempted to make the payment directly to the
cutter and printer. In two cases, the owner insisted that we pay him and that he
pass on the money to the employees, and we acceded to this request.
41. The three-minute targets were chosen after conducting speed tests at two
of the pilot firms mentioned in Section IV. They are approximately 33% higher
than the time to cut a single sheet using the original die and the time to print 48
two-pentagon panels cut using the original die.
42. Because of an error by our enumerators, one firm that was assigned to
Group B was offered the incentive payment intervention. This occurred while two
coauthors were in the field, and the error was caught within hours of its occurrence.
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1143
TABLE VII
COVARIATE BALANCE, INCENTIVE PAYMENT EXPERIMENT
Log avg output/month
Log avg employment
Log avg price, size 5 promo
Log avg price, size 5 training
Avg % promotional (of size 5)
Avg Rs/ball, head cutter
CEO university indicator
CEO experience
Age of firm
N
Group A
incentive
contract
(1)
Group B
no incentive
contract
(2)
9.86
(0.41)
3.35
(0.38)
5.40
(0.02)
6.00
(0.06)
34.90
(6.20)
1.45
(0.10)
0.56
(0.18)
15.50
(3.60)
24.53
(2.83)
15
9.31
(0.29)
3.23
(0.25)
5.45
(0.07)
5.93
(0.06)
32.04
(7.26)
1.63
(0.15)
0.36
(0.15)
16.50
(3.60)
20.60
(2.28)
16
Difference
(3)
0.55
(0.50)
0.13
(0.45)
−0.05
(0.07)
0.07
(0.08)
2.86
(9.60)
−0.17
(0.18)
0.19
(0.23)
−1.00
(5.13)
3.93
(3.64)
31
Notes. Table reports baseline balance in the incentive payment experiment (experiment 2). Sample is the
31 tech-drop firms from the tech-drop experiment that were still active as of September 2013. There are no
significant differences between treatment and control groups. Standard errors in parentheses.
VI.B. Results
Ten of the 15 Group A firms agreed to participate in the experiment.43 Online Appendix Table A.6 reports the times achieved
by the chosen cutter at each firm (all using the two-pentagon
variant of the offset die). The average time was 2 minutes, 52
seconds, approximately 27% longer than the typical time to cut
with the traditional die (2 minutes, 15 seconds). For this reason, we believe that the 100% increase in labor time factored into
the cost calculations in Section II is conservative. In addition,
many cutters expressed confidence that with additional use they
To maintain balance, we randomly selected one as-yet-untreated Group A firm
from the same stratum and reassigned it to Group B. As neither firm adopted, our
results hold with the original randomization (available on request).
43. In 2 of these 10 firms, it was not possible to complete the printer performance test.
1144
QUARTERLY JOURNAL OF ECONOMICS
could lower their cutting time. All printers easily achieved their
target, consistent with the assumption in Section II that despite
some printers’ fears, the offset die does not increase labor time for
printing.
To measure short-run adoption responses, we carried out a
survey round in January–March 2014 (round 6 of our survey),
two to five months after the completion of the incentive payment
intervention. Before turning to the formal regressions, it is useful to examine the raw adoption statistics. Of the 10 Group A
firms that agreed to participate in the experiment, 2 had already
adopted the die. Of the remaining 8 firms, 5 subsequently adopted
using the liberal definition (or 4 using the conservative definition). Of the 16 Group B firms, 3 firms had already adopted prior
to the invention. None of the remaining 13 firms adopted in the
short run, by either measure. To examine medium-run responses,
we carried out another survey round in October–December 2014
(round 7 of our survey), 11–13 months after the completion of the
intervention. If the technology is beneficial, as we have argued,
one would expect it eventually to be adopted by all firms (once
there has been sufficient social learning, for instance) and there
would then be no long-run effect of our incentive treatment. In this
sense, it would be reasonable to expect the medium- and long-run
treatment effect to be smaller than the short-run one. Indeed,
by October–December 2014, one initial nonadopter in Group B
(that is, a Group B firm that had not yet adopted at the start of
experiment 2) had adopted the die (by either definition).
Table VIII uses the liberal adoption measure to assess formally the impact of the incentive payment intervention on adoption rates. All regressions include dummies for the four strata.
Panel A reports impacts on adoption in the short run, and Panel B
in the medium run. Focusing on the short run first, the first-stage
estimates (column 1) indicate, not surprisingly, that assignment
to Group A is significantly associated with greater probability of
receiving the incentive payment treatment; that is, we have a
strong first stage. The dependent variable in columns (2)–(4) is a
binary indicator for whether a firm has adopted. The OLS estimate of the coefficient on treatment in column (2) is positive and
significant, but one might be worried about selection into treatment. The reduced-form (intent-to-treat, ITT) result in column
(3) does not suffer from such selection issues and indicates a
positive and significant (at the 5% level) causal relationship between assignment and adoption. The estimate indicates that the
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1145
TABLE VIII
INCENTIVE PAYMENT EXPERIMENT (LIBERAL ADOPTION MEASURE)
Dep. var.: adoption (>1,000 balls, liberal measure)
First
stage
(1)
Panel A: Short run (as of round 6)
Received treatment
Assigned to Group A
Stratum dummies
Mean of Group B (control
group)
R-squared
N
0.68∗∗∗
(0.12)
Y
0.57
31
Panel B: Medium run (as of round 7)
Received treatment
Assigned to Group A
Stratum dummies
Mean of Group B
(control group)
R-squared
N
0.72∗∗∗
(0.12)
Y
0.60
29
OLS
(2)
0.48∗∗∗
(0.15)
Reduced
form (ITT)
(3)
Y
0.19
0.32∗∗
(0.12)
Y
0.19
0.69
31
0.60
31
0.41∗∗
(0.16)
Y
0.27
0.27∗
(0.14)
Y
0.27
0.61
29
0.52
29
IV
(TOT)
(4)
0.48∗∗∗
(0.15)
Y
0.19
0.69
31
0.37∗∗
(0.17)
Y
0.27
0.61
29
Notes. Table reports results of incentive payment experiment on adoption rates using the liberal definition
of adoption. Panel A reports short-run results as of round 6 (January–March 2014). Panel B reports mediumrun results as of round 7 (October–December 2014). The dependent variable in column (1) is an indicator
for whether the firm received treatment. Two firms exited between rounds 6 and 7. All regressions include
stratum dummies and report robust standard errors. Significance: ∗ .10, ∗∗ .05; ∗∗∗ .01.
probability of adoption increased by 0.32 among those assigned to
treatment (from a baseline of 13%).44 The IV estimate (the effect
of treatment on the treated) is substantially higher: 0.48. However, since the one third of firms who refused the intervention may
44. An interesting question to consider is how our estimates would differ if we
had included the non-tech-drop firms in our second experiment—that is, if we had
offered the technology and incentive payment at the same time to firms that had
not received the offset die initially. We suspect that the ITT estimate would have
been larger among these firms. Among the original tech-drop firms, cutters that did
not reveal that the technology was a good one before experiment 2 may have been
particularly reluctant to do so in experiment 2, since it would have revealed that
they had previously been misreporting the benefits of the technology. By contrast,
if we had given the technology and incentive simultaneously to the original nontech-drop firms, cutters in those firms would have had no reason to be reluctant.
1146
QUARTERLY JOURNAL OF ECONOMICS
TABLE IX
INCENTIVE-PAYMENT EXPERIMENT (CONSERVATIVE ADOPTION MEASURE)
Dep. var.: adoption (>1,000 balls, cons. measure)
First
stage
(1)
Panel A: Short run (as of round 6)
Received treatment
Assigned to Group A
Stratum dummies
Mean of Group B
(control group)
R-squared
N
0.68∗∗∗
(0.12)
Y
0.57
31
Panel B: Medium run (as of Round 7)
Received treatment
Assigned to Group A
Stratum dummies
Mean of Group B
(control group)
R-squared
N
0.72∗∗∗
(0.12)
Y
0.60
29
OLS
(2)
0.45∗∗∗
(0.16)
Reduced
form (ITT)
(3)
Y
0.12
0.31∗∗
(0.12)
Y
0.12
0.56
31
0.46
31
0.39∗∗
(0.17)
Y
0.20
0.26∗
(0.14)
Y
0.20
0.46
29
0.38
29
IV
(TOT)
(4)
0.46∗∗∗
(0.16)
Y
0.12
0.56
31
0.35∗
(0.19)
Y
0.20
0.45
29
Notes. Table reports results of incentive payment experiment on adoption rates using the conservative
definition of adoption. Panel A reports short-run results as of round 6 (January–March 2014). Panel B reports
medium-run results as of round 7 (October–December 2014). The dependent variable in column (1) is an
indicator variable for whether the firm received treatment. Two firms exited between rounds 6 and 7. All
regressions include stratum dummies and report robust standard errors. Significance: ∗ .10, ∗∗ .05; ∗∗∗ .01.
have chosen to do so because of particularly large costs of adoption
(or small benefits), these IV estimates should be treated with caution. The medium-run ITT estimate in Panel B is slightly lower
at 0.27 (and statistically significant at the 10% level) because of
the one Group B firm that adopted in the second six-month period
after the intervention. (The number of observations falls in the
medium-run results because two firms exited.) Table IX reports
very similar results using the conservative adoption measure—
the short-run ITT is 0.31 and statistically significant at the 5%
Because of this difference, treating the non-tech-drop firms in experiment 2 would
not have represented a simple increase in sample size.
1147
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
TABLE X
INCENTIVE PAYMENT EXPERIMENT RESULTS (DIE PURCHASE AS OUTCOME)
Dep. var.: purchased offset die after Sept. 1, 2013
First
stage
(1)
Panel A: Short run (as of round 6)
Received treatment
Assigned to Group A
Stratum dummies
Mean of Group B
(control group)
R-squared
N
0.68∗∗∗
(0.12)
Y
0.57
31
Panel B: Medium run (as of round 7)
Received treatment
Assigned to Group A
Stratum dummies
Mean of Group B
(control group)
R-squared
N
0.72∗∗∗
(0.12)
Y
0.60
29
OLS
(2)
0.42∗∗
(0.15)
Reduced
form (ITT)
(3)
Y
0.00
0.27∗∗
(0.12)
Y
0.00
0.40
31
0.24
31
0.41∗∗
(0.15)
Y
0.00
0.28∗∗
(0.12)
Y
0.00
0.40
29
0.24
29
IV
(TOT)
(4)
0.40∗∗
(0.16)
Y
0.00
0.40
31
0.38∗∗
(0.16)
Y
0.00
0.40
29
Notes. Table reports results of incentive payment experiment on adoption rates using additional die
purchases (beyond the trade-in offer) after September 1, 2013, as the measure of adoption. Panel A reports
short-run results as of round 6 (January–March 2014). Panel B reports medium-run results as of round 7
(October–December 2014). The dependent variable in column 1 is an indicator variable for whether the firm
received treatment. Two firms exited between rounds 6 and 7. All regressions include stratum dummies and
report robust standard errors. Significance: ∗ .10, ∗∗ .05; ∗∗∗ .01.
level, and the medium-run ITT is 0.26 and statistically significant
at the 10% level.45
To check robustness, Table X reports results using an alternative indicator of adoption: whether the firm purchased its first
offset die (beyond the trade-in that we paid for) after September
1, 2013. Of the eight firms that accepted the intervention and
45. Online Appendix Tables A.7–A.9 rerun the results using more stringent
adoption thresholds of 5,000, 10,000, or 20,000 balls, respectively (as opposed to
the 1,000 ball threshold used in Tables VIII–IX). The results are even stronger
because no control firm is classified as an adopter using these cutoffs. Note that
the adoption rates are the same for the 5,000 and 10,000 cutoffs, so the numbers
in those tables are identical to each other.
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QUARTERLY JOURNAL OF ECONOMICS
had not adopted by August 2013, three subsequently purchased
their first offset die. (One of these firms had not yet produced with
it at the time of our October–December 2014 survey round.)46
Table X shows that the positive causal effect of the incentivepayment treatment on adoption is robust to using this alternative
measure. As before, we report both short- and medium-run impacts. The short- and medium-run ITT estimates are 0.27 and
0.28, respectively, and are statistically significant at the 5% level.
It is important to acknowledge that the sample sizes in
the incentive payment experiment are small. An alternative to
large-N statistical inference are permutation tests whose properties are independent of sample size. (See Bloom et al. 2013 for
the use of this type of inference in a similar context.) We determine the proportion of all 25,872,000 possible treatment assignments that would produce coefficients as large as or larger
than the ones we find (holding the observed outcome for each
firm unchanged).47 This procedure does not require asymptotic
approximations. Given the selection discussion above, we focus
on the ITT estimates in column (3) of Tables VIII–X. Online Appendix Figure A.11 plots the distribution of coefficients obtained
from regressing the liberal adoption measure on assignment to
Group A for the millions of possible treatment assignments. Because of the small number of adopters, there are only a handful of possible coefficients, despite the large number of possible
assignments. For the short run, our estimated ITT is the most
extreme coefficient that could be observed under any treatment
assignment and is equal to the critical value for a 1% significance
test. For the medium run, where we would expect the treatment
effect to have begun to attenuate, a fraction 0.061 of assignments
are more extreme and our point estimate is equal to the critical
value for a 10% significance test. Online Appendix Figure A.12
reports the corresponding plots using the conservative adoption
measure. Our short-run ITT estimate lies between the 1% and 5%
46. In addition, one large Group A firm that was already classified as an
adopter because it was using the offset cutting pattern for table cutting (see note
16), purchased its first die (beyond the four-panel offset die we originally gave)
following the beginning of our intervention.
47. Within each of the four strata, we assigned treatment status with equal
proportion. The stratum of smaller firms contained 6 firms, the stratum of larger
firms contained 12 firms, the stratum of initial nonresponders contained 8 firms
and the stratum of already-adopters contained 5 firms. This means there are
8 5
5
25,872,000 = (63 )(12
6 )(4 )((2 ) + (3 )) possible treatment assignments.
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1149
critical values, with a fraction 0.018 of assignments being more
extreme. Our medium-run ITT estimate lies just below the 10%
critical value with a fraction 0.118 of assignments being more
extreme. Online Appendix Figure A.13 presents a similar analysis for our alternative indicator of adoption: die purchases. The
short-run ITT estimate is the most extreme outcome possible and
is equal to the 1% critical value. The medium-run ITT estimate is
equal to the 5% critical value, with a fraction 0.036 of assignments
being more extreme.
To sum up: although the level of significance dips slightly below 90% in one of the specifications (using the more conservative
adoption measure and the small-sample-robust permutation test
in the medium-run specification, when we would expect the effect
to have begun to attenuate), overall the results strongly suggest
that, consistent with our theoretical model of misaligned incentives, the incentive payment treatment spurred adoption among
firms.
VII. ADDITIONAL EVIDENCE ON MECHANISMS
In this section, we present additional (nonexperimental) evidence on two key features of our theoretical model: wage stickiness
and information flows within the firm. So as not to reveal the existence of the offset die to non-tech-drop firms, most of the questions
we discuss were asked only of the 31 tech-drop firms involved in
the second experiment, and we focus on these tech-drop firms
throughout this section. (This section focuses on responses from
round 7 of our survey, conducted in October–December 2014.)
To shed light on wage stickiness, we recorded the wages of
cutters and printers for each month between August 2013 and
September 2014. Generally, it appears that firms change wages
relatively seldom. Of the 24 tech-drop firms that provided complete information, 10 did not change the head cutter’s wage at all
over the entire 13-month period, a period of 8.4% annual inflation
(Online Appendix Table A.10). Thirteen out of 24 did not change
the wage for the head printer. This evidence is corroborated by the
survey responses from the head cutters and printers themselves:
only 2 out of 15 cutters who responded indicated that their wage
had changed during this period, and only 4 out of 17 printers reported a change. We asked firms why they changed wages, and the
responses for the few firms that provided answers are in Online
Appendix Table A.11. The two main reasons cited by owners were
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QUARTERLY JOURNAL OF ECONOMICS
(a) inflation and (b) the change was a regular “end of year” change.
Only one firm reported changing wages because of the offset die.
In addition to the reasons discussed already, another possible reason for the wage stickiness is that it is conventional to use round
numbers for piece rates. Online Appendix Figure A.14 shows that
78% of piece rates are 1, 1.5, 2, 2.5, 3, or 4 rupees per ball. Given
this convention, it may be that wage increases must be reasonably
large when they occur. These several pieces of evidence reinforce
the idea that piece rates are sticky, consistent with the theoretical ideas (i) that they are chosen in stage 1 of the game, prior to
the technology arriving, and (ii) that there is a significant cost to
adopting contracts that involve changing piece rates.
We also asked owners and head cutters directly why payments were not conditioned on the technology and whether such
a possibility was ever discussed. The reason cited most often by
owners (6 out of 18) was that offering such incentives would lead
workers to expect additional incentives in the future. The next two
most common answers were that workers would perceive these incentives as unfair or that the owners did not think of it (Online
Appendix Table A.12). Cutters overwhelmingly stated that they
feel it is not their place to make suggestions to the owner about
the payment schemes (Online Appendix Table A.13). In almost
no case did the owner report discussing payments to adopt the
die with any employee (Online Appendix Table A.14). Similarly,
cutters never reported talking to the owner or other co-workers
about payments to adopt the die. These responses are consistent
with both of the hypotheses for lack of adjustment of contracts:
that owners and cutters were largely unaware of the possibility
of conditional contracts, or that both were aware of the contracts
but there were transaction costs associated with adopting them.
Turning to information flows within the firm, we asked owners if they had ever had a conversation with their head cutter,
other cutters, or head printer about whether to adopt the offset
die.48 It appears that this question did not capture all forms of
communication between owners and cutters. For instance, of the
10 firms that reported “cutters unwilling” as the main reason for
48. The exact wording of the questions was “Did you have a conversation with
[the following employee] about whether you should adopt the offset die?” and “If
yes, what did they say?” with response codes “(a) the technology is beneficial and
should be adopted, (b) the technology is not beneficial and should not be adopted,
(c) not sure whether the technology is beneficial or not, (d) other (
).”
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1151
not adopting the technology in round 4 of our survey (Table VI),
only 6 reported that they had had a conversation with the head
cutter about whether to adopt the die. Presumably the remaining respondents received signals from cutters through less direct
means or were too proud to admit to taking advice from subordinates. With this caveat, the responses do suggest that owners
were swayed by the opinions of cutters in their adoption decisions. Ten of 22 respondents reported consulting with their head
cutter about the decision (Online Appendix Table A.15). There is a
clear association between what the cutter recommends and what
owners eventually do. Online Appendix Table A.16 reports the
recommendations given by the cutters in the 10 cases where owners report consulting them, and the firms’ corresponding adoption
decisions using the liberal definition. In all three instances where
the head cutter recommended adopting, the firm adopted; in four
of the six instances where the head cutter’s recommendation was
negative, the firm did not adopt.49 These responses are consistent
with the assumption in the model that owners are less informed
than cutters, and the result that in some circumstances the principal finds it worthwhile to follow the cutter’s advice.
As mentioned, additional corroborative evidence for our story
comes from firms’ experience with a previous innovation in the
cutting process. Prior to 1994, all firms used a single-panel
die to cut pentagons. In 1994, the first firms began using the
two-panel nonoffset pentagon die pictured on the right side of
Figure I, referred to in Sialkot as the back-to-back die. This die is
much faster than a single-pentagon die, since each strike by the
hydraulic press cuts two pentagons, and the die does not appear to
have reduced the number of pentagons per sheet. Owners report
that the back-to-back die was adopted much more quickly than
our offset die has been, and with little resistance from cutters. All
14 owners who could recall the speed of adoption indicated that
they adopted the die within six months of hearing about it (Online
Appendix Table A.17). Furthermore, 23 out of the 24 respondents
report there was no resistance among cutters to adopt this die
(Online Appendix Table A.18) and very few firms changed their
payment structure in any way (Online Appendix Table A.19). Relating this evidence to our theory, the back-to-back die can be interpreted as corresponding to technology type θ 3 (equal marginal
49. Using the conservative definition, in five of the six cases the firm did not
adopt.
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cost but faster than the existing technology), which both principal and agent want to adopt. The fact that the back-to-back die
was adopted quickly and with little resistance from cutters reinforces two ideas from the theory: first, that cutters’ and owners’
interests over some new technologies coincide (a precondition for
informative communication); and second, that the misalignment
of incentives for our offset die is (at least in part) responsible for
the slow adoption of it.
VIII. ALTERNATIVE HYPOTHESES
Here we examine what we consider to be the two leading
competing explanations for the results of our second experiment:
(i) that we mechanically induced firms to adopt by subsidizing
the fixed costs of adoption, rather than by aligning incentives and
inducing information flows within firms; and (ii) that we simply
increased the salience of the new technology and this in itself led
firms to adopt.
VIII.A. Subsidies for Fixed Costs
Suppose in our model that the principal and agent had the
same information about the technology. The owner would then
simply weigh the expected variable cost reduction from the new
technology against the fixed costs of adoption, F. These fixed costs
might include a lump-sum bonus to workers to compensate them
for a learning period in which their incomes are reduced. Our incentive payment experiment could be seen as providing a subsidy
for these fixed costs, and the subsidy may itself have induced the
owner to adopt.
Is this a quantitatively plausible explanation of our findings?
To organize our thinking about this question, we can write the
present discounted value of expected additional profit from adoption for firm f as follows:
(1)
f = −F f + P
∞
NV B f
NV B f
,
=
−F
+
P
f
(1 + r)t
r
t=1
where f is expected profit; Ff is the fixed cost of adoption, which
must be paid up front and may be firm-specific; P is the probability
perceived by the firm that the technology works as we said it does;
NVBf are net variable benefits per cutter per month; and r is the
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1153
market interest rate.50 Net variable benefits can be calculated for
each firm following the method of Section II.D; we assume these
are constant over time. For firms that did not have to buy the die
(i.e., the tech-drop firms) a conservative estimate of “observed”
fixed costs is Rs 10,800/US$108 to cover the cost of making new
screens and purchasing a new combing die. (See Section II.D.) In
this section, we make the assumption that the Rs 32,000/US$320
we paid in the incentive payment treatment were also necessary
and part of the true fixed cost of adoption.51 We thus take the
total observed fixed costs to be Rs 42,800/US$428.52 Including the
incentive payments in fixed costs seems conservative; in the incentive payment experiment, only one worker felt that the payment
was insufficient to cover his costs of adoption, suggesting that the
incentive payments were greater than the training cost faced by
workers in the great majority of firms.
As a first step toward answering the quantitative plausibility
question, suppose that there is no uncertainty about the technology (i.e., P = 1) and no unobserved fixed costs (i.e., no fixed costs
beyond the US$428). Under these assumptions, equation (1) can
be reconciled with nonadoption only if firms face extremely high
interest rates and hence have a small effective discount factor
1 53
. The values of firm-specific interest rates that set f = 0 in
1+r
equation (1) represent lower bounds on the firm-specific interest
rates for nonadopters. At the 10th percentile, the lower bound is
50. Here we are abstracting from the possibility that firms can learn from
others about the profitability of adoption, which would tend to lead them to delay
adoption (and wait for others to adopt), as emphasized by Foster and Rosenzweig
(1995). Such an effect could help to explain low initial adoption, but not the large
increase in adoption in response to our second experiment, and the argument of
this section that our subsidies were too small to generate the latter would continue
to hold in a more complex social-learning model.
51. As discussed in Section VI.A, we paid Rs 15,000/US$150 to the cutter,
Rs 12,000/US$120 to the printer, plus Rs 5,000/US$50 to the owner to cover overhead. In Section II.D, we assumed that workers’ wages went up by 100% to compensate them for using the slower technology. The discussions in Sections V and
VII suggest that many firms did not adjust their wages and so here we assume
that firms make no changes in piece rates but make a one-off lump-sum payment
to cutters and printers of the same size as in our experiment 2.
52. We take these to be the fixed costs per cutter. This is a conservative
assumption because it is possible that firms could, for example, share the combing
machine across cutters.
53. This is another way of stating the argument from Section II.D that the
observable fixed costs of adoption can be recouped within a relatively short amount
of time by almost all firms.
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QUARTERLY JOURNAL OF ECONOMICS
13.8% per month; at the 90th percentile, the lower bound is 134%
per month. We asked firms explicitly about the interest rates they
face, and the responses ranged from 9% to 25% per year; these
are an order of magnitude lower than the implied lower bound for
most firms. That is, in the absence of both uncertainty about the
technology and unobserved fixed costs, the interest rates (or rate
of discounting) that would be required to explain the low initial
rates of adoption appear to be implausibly high.
This argument leaves open the possibility that some combination of uncertainty and unobserved fixed costs can account
for the low initial rates of adoption we observed. To address this
possibility, we take a different approach: we allow for both uncertainty and unobserved fixed costs and ask whether these can
explain both the low rates of initial adoption and the magnitude
of the response to our incentive payment intervention. The unobserved fixed costs may represent attention costs for the owner
or psychic costs involved in changing established routines. We assume, conservatively, that the interest rate is the highest of the
self-reported interest rates, 25% a year. As a benchmark, we assume that owners place a 50% probability on the event that the
technology works as we described (i.e., P = 0.5); we consider alternative priors below. Under these assumptions, we can place
bounds on the values of unobserved fixed costs that can explain
the behavior we observe. In particular, from equation (1), if a firm
does not adopt initially, it must be that f < 0 and hence that
NV B
F f > (0.5) 0.0187f . (A 25% annualized interest rate corresponds to a
1.87% monthly rate.) If a firm adopts in response to the incentive
payment experiment, it must be that f > 0 after the US$320 reNV B
duction in fixed costs, and hence that F f < (0.5) 0.0187f + 320. These
bounds on fixed costs are plotted by rank in Figure V for the
31 firms in the incentive payment experiment (using the liberal
adoption measure). The solid black bars indicate upper bounds on
fixed costs for the initial adopters (for which we can only calculate upper bounds). The white bars indicate lower bounds for the
never-adopters (for which we can only calculate lower bounds).
The solid gray bars indicate lower bounds for initial nonadopters
that adopted in response to the incentive treatment (i.e., compliers in experiment 2); the black outlines above the solid gray bars
indicate upper bounds for these firms. The figure highlights that
the US$320 subsidy (indicated by the distance between the top of
the solid gray bars and the black outlines just above them) is small
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1155
FIGURE V
Implied Bounds on Fixed Costs under a Learning Subsidy Explanation
Figure displays implied bounds on fixed costs for each firm in the incentive payment intervention, assuming a 25% annual interest rate and a prior of 0.5 that the
technology works (see Section VIII.A). Solid black bars indicate upper bounds for
the initial adopters, and white bars indicate lower bounds for the never-adopters.
Solid gray bars indicate lower bounds for initial nonadopters that adopted in response to the incentive treatment (experiment 2 compliers); black outlines above
the solid gray bars indicate upper bounds for these firms. (Unobserved fixed costs
can simultaneously explain both initial nonadoption and subsequent adoption for
these firms only if they fall between top of gray bar and black outline.)
relative to the implied lower bound on fixed costs for almost all
initial nonadopters.
In this context, the key question is whether a plausible distribution of unobserved fixed costs is consistent with the adoption
responses and implied bounds illustrated in the figure. It turns
out that if we impose even minimal structure on the distribution of
unobserved fixed costs then it is very unlikely that the distribution
would generate both the initial lack of adoption (which requires
that unobserved fixed costs be large) and the large response to
the second experiment (which requires that US$320 make a significant difference to the adoption decisions of a large number of
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QUARTERLY JOURNAL OF ECONOMICS
firms). Suppose that fixed costs are distributed log-normally:
(2)
ln(F f ) = μ + ε f ,
where ε f ∼ N (0, σε2 ). Using maximum likelihood, we can estimate
the values of μ and the error variance, σε2 , that can best account for the observed adoption responses in both experiments.54
Table XI, Panel A reports these estimates μ̂ and σ̂ε2 under six priors
ranging from P = 0.01 to P = 1. With these estimates in hand, we
can ask: what is the ITT estimate we would obtain from a US$320
subsidy on adoption, the size of the payment in experiment 2?
To answer this question we use μ̂ and σ̂ε2 to simulate fixed cost
draws 1,000 times, with Online Appendix Figure A.15 displaying
the full distribution of Group A firms switching from nonadoption
to adoption as a result of a US$320 payment. In Table XI, Panel
B, we report the average ITT estimate and the standard deviation from these simulations. The ITT estimates range between
0.01 and 0.10 for priors between P = 1 and P = 0.05, much lower
54. We use the short-run liberal adoption rates for this exercise. Let adopt1f
denote an indicator if firm f had adopted the technology in experiment 1, and
adopt2f an analogous indicator for adoption after experiment 2. The log likelihood
function for Group A firms is:
Bf
ln P NV
+320 −μ 0.0187
(3) L(μ, σε ) =
f (1 − adopt1 f )(adopt2 f ) ln σε
NV B f
ln P 0.0187 −μ −
σε
ln
+ (1 − adopt1 f )(1 − adopt2 f ) ln 1 − ln
+ (adopt1 f ) ln P
NV B f
0.0187
σε
P
NV B f
0.0187
+320 −μ σε
−μ .
The first expression on the right-hand side of equation (3) captures the contribution
to the likelihood of initial nonadopter Group A firms who are induced to switch
from nonadoption to adoption because of the $320 subsidy. The second expression
captures initial nonadopter Group A firms who are not induced to switch adoption
status. The third expression is the contribution of initial adopter firms. For Group
B firms, the likelihood function is the same except adopt2f = 0 ∀f and there is
no US$320 payment inside the argument of the normal CDF, (.). We pool both
groups when maximizing the likelihood function.
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1157
TABLE XI
QUANTITATIVE PLAUSIBILITY OF LEARNING SUBSIDY EXPLANATION
Value of prior for whether offset die
works as described
0.01
(1)
0.05
(2)
0.1
(3)
0.25
(4)
0.5
(5)
1
(6)
7.41∗∗∗
(0.30)
1.29∗∗
(0.48)
31
8.00∗∗∗
(0.29)
1.23∗∗∗
(0.44)
31
8.85∗∗∗
(0.28)
1.21∗∗∗
(0.41)
31
9.53∗∗∗
(0.28)
1.20∗∗∗
(0.40)
31
10.21∗∗∗
(0.28)
1.20∗∗∗
(0.40)
31
0.10
(0.09)
31
0.06
(0.06)
31
0.03
(0.04)
31
0.01
(0.03)
31
0.01
(0.02)
31
Panel C: p-values of observing 5 adopters in incentive payment experiment
.234
.013
.000
.000
.000
.000
Panel A: Estimates of fixed costs
Estimate of μ
6.46∗∗∗
(0.50)
1.87∗∗
Estimate of σ ε
(0.75)
N
31
Panel B: ITT estimate
Assigned to Group A
N
0.24
(0.11)
31
Notes. Sample is tech-drop firms still active at the time of second experiment (September 2013). Panel A
reports estimates for μ and σ ε from maximizing the likelihood function in equation (3) for the value of prior
noted in the column heading. Panel B reports the average and standard deviation of the ITT estimates across
1,000 simulation draws of log fixed costs from a normal distribution with mean μ̂ and standard deviation σ̂ε
for the corresponding prior. Panel C reports the p-values of observing five or more adopters in the short run
(recall that we observed five adopters in the short run according to the liberal-definition adoption variable),
based on the frequency of occurrence in the 1,000 simulations, under the null hypothesis that the second
experiment only changed fixed costs. Significance: ∗ .10, ∗∗ .05, ∗∗∗ .01.
than the 0.32 we obtained in our incentive payment experiment
(see Table VIII, Panel A, column (3)).55 Even for the most pessimistic prior of P = 0.01, we only obtain an ITT of 0.24. Although
such a low prior is theoretically possible, it seems unrealistically
pessimistic given the nature of the technology.56 In summary, it
55. Table XI, Panel C reports the corresponding probabilities that five or more
Group A firms adopt in the short-run (recall that the incentive payment treatment
induced five adopters according to the liberal adoption measure). The probability
of observing an increase in adoption of that magnitude is exceedingly low, between
0.000 and 0.013, for all but the most pessimistic prior of P = 0.01.
56. Recall that we visited each treatment firm and performed a demonstration
of the technology in front of the owner. This demonstration involved having the
firm’s cutter cut using the traditional die and count the pentagons and then cut
using the offset die and count the pentagons. The latter was repeated until the
cutter achieved 272 pentagons from a sheet. In no case did this require more
than three sheets. The nature of the technology is sufficiently transparent that,
even if the owners were not convinced of all aspects of the technology, it seems
to us implausible that they placed less than 1% probability on the fact that the
technology worked as we said it did.
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QUARTERLY JOURNAL OF ECONOMICS
appears that if unobserved fixed costs are the reason for low adoption in experiment 1, a US$320 subsidy alone cannot plausibly
explain the magnitude of the increase in adoption we observed in
experiment 2.
VIII.B. Salience
A second alternative explanation is that the incentive payment treatment increased the salience of the new technology and
that this itself led firms to adopt, independent of any effects on
information flows within the firm. There are two variants of this
explanation. One variant is that the reminder about the technology during the incentive-intervention visit “nudged” firms into
adoption. This variant seems an unlikely explanation. We gave
the same reminder to the control firms for the incentive intervention (Group B firms) and we saw no firms adopt in the next six
months as a result. Also, it is worth noting that we visited all of
the tech-drop firms multiple times in our survey rounds. During
each visit, we discussed the new technology with them and if they
were not using it we asked why.
A subtler variant of the salience story is that by putting more
money on the table we sent a stronger signal about our own beliefs about the efficacy of our technology, and that this in turn
led firms to update their priors about the technology, inducing
some to adopt. While this explanation is sufficiently flexible that
it is difficult to dismiss definitively, it also seems unlikely to have
played a major role. We believe that it was clear to firms from
the outset, in the initial technology-drop implementation, that we
thought that the technology was effective. We then returned to
each firm numerous times and in the case of the tech-drop firms
discussed the offset die each time. The amount of money we spent
on surveying each firm far exceeded the US$320 payment in the
incentive intervention. In short, although in retrospect it seems
clear that many owners did not believe us when we told them that
the technology works, it does not appear that their skepticism
was based on their beliefs about how strongly we held our beliefs,
or that the US$320 affected their beliefs about how strongly we
held our beliefs. It seems more likely that they simply thought
all along that we had insufficient knowledge and experience in
the industry, and hence that our (strongly held) confidence in the
technology was misplaced.
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1159
IX. CONCLUSION
This article has two basic empirical findings. First, despite
the evident advantages of the technology we invented, a surprisingly small number of firms have adopted it, even among the set
of firms we gave it to. This is consistent with a long tradition of
research that has found diffusion to be slow for many technologies, but given the characteristics of our technology—low fixed
costs, minimal required changes to other aspects of the production
process, easily measured cost advantages—the low adoption rate
seems particularly puzzling. Second, with a modest change to the
incentives of key employees in the firm—very small in monetary
terms relative to firms’ revenues and the benefits of adoption—
we induced a statistically significant increase in adoption. This is
consistent with the hypothesis that a misalignment of incentives
within the firm is an important barrier to adoption. Although we
do not observe all of the communication between employees and
owners, it appears that at least one way that employees have resisted the adoption of our new technology is by misinforming owners about the value of the technology. It further appears that the
incentive payment intervention had a significant effect because it
induced workers to reveal the benefits of the technology.
A natural question is why many owners did not simply change
the payment scheme on their own, to give employees a greater
incentive to adopt the new technology. Survey evidence indicates
that some firms are not aware of the availability of alternative
payment schemes, and that many firms believe that there are
nontrivial transaction costs involved in changing contracts, even
implicit ones. One would expect owners to weigh any costs of
modifying contracts against the expected benefits of adopting new
technologies. If owners have low priors that a new technology
is beneficial (or that a beneficial new technology will arrive in
the future), they may rationally be unwilling to pay even quite
small transaction costs. Although it is difficult for us to distinguish
empirically between lack of awareness of alternative contracts
and stickiness of contracts as explanations for why owners did
not change payment schemes, the key point is that many owners
did not in fact change schemes, and this left scope for our very
modest incentive intervention to have a large effect on adoption.
Our study carried out a particular intervention with a particular technology in a particular sector. This naturally raises
questions about external validity. For example, both of our
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experiments were relatively hands-off: we gave limited advice to
firms after the initial demonstration in the first experiment, or
the incentive payment in the second. Would organizational barriers still hinder adoption if the innovators (or their salespeople)
had financial incentives to work with firms to promote adoption?
We strongly suspect so, since there would still be costs associated
with overcoming such barriers, and it is not clear that innovators’
incentives would be strong enough to induce them to pay such
costs for firms, or that the firms themselves would be willing ex
ante to grant innovators the access and time to identify the barriers and resolve them (especially if they receive negative feedback
about the technology from their own workers).57 Nevertheless, we
recognize that more research is required, in other sectors, with
other technologies, to determine to what extent our findings in
Sialkot can be generalized.
With that caveat, we believe that three implications of our
study are likely to apply more broadly. First, we have provided
reasonably direct evidence of a complementarity, in the sense of
Milgrom and Roberts (1990, 1995), between a technological innovation (the offset die) and an organizational innovation (conditional wage contracts). We suspect that similar complementarities
between technical and organizational innovations exist in many
other settings, and that it is common for technology adoption to
require organizational changes to be successful.
Second, there appears to be a form of inertia in employment
relationships that can hinder technological change. We have argued that firms’ choices of labor contracts depend on the rate
at which beneficial new technologies are expected to arrive. It
also appears that labor contracts, once established, are difficult to
modify. The implication is that firms and industries that evolve in
technologically stable environments may be less able to adapt to
technological change than new firms and industries, or firms and
industries that evolve in technologically dynamic environments.
Simple piece-rate contracts may well have been optimal for firms
in Sialkot before we showed up, but the very fact that firms in
Sialkot have been producing for decades using them may itself
have contributed to low adoption rates of the offset die.
57. We also believe that our results are directly relevant to a number of realworld settings in which innovations are developed by nonprofits and public sector
entities (e.g., agricultural extension services) that have weak financial incentives
but that nonetheless aim to promote adoption of beneficial new technologies.
ORGANIZATIONAL BARRIERS TO TECHNOLOGY ADOPTION
1161
Finally, it seems likely that for technology adoption to be successful, employees need to have an expectation that they will share
in the gains from adoption. Such an expectation may be generated
by a variety of different types of contracts, implicit or explicit. But
to the extent that firms must rely on the knowledge of shopfloor
workers about the value of new technologies or how best to implement them, it appears to be important that some sort of credible
gain-sharing mechanism be in place.
MIT
LAHORE SCHOOL OF ECONOMICS
LAHORE SCHOOL OF ECONOMICS
COLUMBIA UNIVERSITY
COLUMBIA UNIVERSITY
SUPPLEMENTARY MATERIAL
An Online Appendix for this article can be found at The Quarterly Journal of Economics online.
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